Andreas Maier

Prof. Dr.-Ing. habil. Andreas Maier

Head

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 09.138
Martensstraße 3
91058 Erlangen
Germany

Appointments

nach Vereinbarung/by agreement


https://medium.com/@akmaier
ORCID iD iconhttps://orcid.org/0000-0002-9550-5284

Academic CV

Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool – PEAKS – that has been used to analyze over 4.000 patient and control subjects so far.

From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit.

In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant “4D nanoscope”.  Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

Projects

2023

  • Artificial Intelligent as a Market Participant – Implications for Antitrust Law

    (FAU Funds)

    Term: January 15, 2023 - January 14, 2024

    Introduction: Antitrust laws (also known ascompetition laws) are designed to encourage strong competition and are designedto protect consumers from predatory commercial practices. The primary goals ofantitrust law are to ensure the functioning of the markets and to ensure faircompetition. A prominent example of an antitrust violation is illegal pricefixing. By definition, it is an agreement between competitors that fixes pricesor other competitive conditions, and thus violates the principle of the pricingmechanism through free market forces. A typical feature of illegal price fixingis verifiable communication (written or verbal) between human marketparticipants. However, in the age of artificial intelligence and e-commerce,the definition and the detection of this illegal practice faces new challengesas collusive behaviors that violate antitrust laws, such as the pricingmechanism, can be partially or fully automated [1]. Furthermore, thecommunications between market participants can be both overt and covert. Finally,market participants can be artificial agents which might affected by perverseinstantiation [2]. In other words, new technological possibilities areavailable to disguise illegal pricing policies and business practices.

    Recent research, mainly from theeconomic and jurisprudence point of view, concludes the intensive applicationof AI algorithms in E-commerce will increase the extend of known forms ofanticompetitive behaviors [3][4]. However, the questions regarding whether andto which extent collusive behaviors will emerge by AI itself (which is anunknown form of anticompetitive behaviors) are rarely understood. Feasibilitystudies and comprehensive analysis comprising the implementation of AI methods andvalidation of the derived hypothesis has not been conducted so far. Therefore,the main goals of this project are to investigate the possibilities of collusivebehaviors stimulated and/or emerged by AI algorithms on digital marketplace andderive consequences on the antitrust law as well as competition policies. Tothe best of our knowledge, this is the first time that a research project inthe field of Antitrust and AI (AAI) is focusing on the mathematical andalgorithmic perspective of the question to which extend the utilization of AImethods is facilitating the collusive behaviors in the era of digital economy.

    Objectives: In order to validate the hypothesesthat AI algorithms is able to develop and communicate collusive behaviors ondigital marketplaces both in overt and covert fashion, comprehensive emulatorsof online marketplaces in different setups will be implemented.  Furthermore, different communication channels(both overt and covert) of digital marketplaces will be discovered and understood,as it is highly relevant to the detection of collusive practices. Finally, differentonline trading scenarios utilizing AI algorithms will be established and theimpact on antitrust law and competition polices will be derived. In total, the mainaspects in the intended DFG-application can be summarized as follows:

    1.      Asthe research topic belongs to a highly interdisciplinary field, a comprehensiveliterature review is necessary to define the problem space of the research andis of great importance to conduct the subsequent experiments successfully.Therefore, a comprehensive literature review on the aspects of antitrust law, gametheory, artificial intelligence and cyber security will be conducted.

    2.      Firststep of the implementation is the holistic emulation of the digitalmarketplace. The market emulator should have the capability to emulate the digitalmarket following various rules (e.g., Cournot vs. collusive competition) indifferent size (i.e., with different amount of market participants). Moreover,state-of-the-art algorithms for dynamic pricing should be replicated andintegrated into the market emulator as well.

    3.      Afurther aspect of this project is the communication mechanism in the era of E-commerceand AI. The know form of collusions mostly utilize overt communications.However, covert communication channels (i.e., communication channels that are notoriginally designed for the communication purpose, therefore hardly to bedetected [5][6]) poses further vulnerabilities of online marketplaces. The mechanismsand capacities of covert channels facilitating the collusive behaviors (e.g.,illegal price fixing) as should be investigated with the implemented marketemulator.

    4.      Finally,artificial agents for price definition of different products should be proposedand implemented following different competition models as well as marketcomplexities, aiming at understanding the central research questions of thisresearch project, i.e., capabilities and conditions of emerging collusivebehaviors of artificial agents by themselves. This particular step can beachieved by using reinforcement learning techniques. Technical opportunitiesand challenges for the discrimination of collusive and non-collusive behaviorsthat are potentially emerged by the artificial agents should be explored aswell.

    The entire project will besupervised by experts from three disciplines. Prof. Jochen Hoffmann (chair of Private Business Law) will support this research project with his knowledges and expertiseon antitrust law, Prof. Felix Feilling (chair of Cyber Security) will advise onthe aspects that are related to covert communication and cyber security, andProf. Andreas Maier (pattern recognition lab) will mentor this project from theAI point of view.

    [1] KünstlicheIntelligenz als Marktteilnehmer – Technische Möglichkeiten, Maier A., Bayer S.,Mohr Verlag. Submitted, unpublished.

    [2] BostromN. Superintelligence: Paths, Dangers, Strategies. Minds & Machines 25, Seite 285–289 (2015).

    [3] Petit, N. Antitrust andartificial intelligence: A research agenda. In: Journal of European CompetitionLaw and Practice. Vol. 8, Issue 6, pp. 361–362. Oxford University Press.(2017)

    [4] Beneke, F., Mackenrodt, M.,Remedies for algorithmic tacit collusion, Journal of Antitrust Enforcement,Volume 9, Issue 1, Pages 152–176 (2021).

    [5]Hans-Georg Eßer, Felix C. Freiling. Kapazitätsmessung eines verdecktenZeitkanals über HTTP, Univ. Mannheim,Technischer Bericht TR-2005-10, November 2005. (2005)

    [6] Freiling F.C., Schinzel S.Detecting Hidden Storage Side Channel Vulnerabilities in NetworkedApplications. In: Camenisch J., Fischer-Hübner S., Murayama Y., Portmann A.,Rieder C. (eds) Future Challenges in Security and Privacy for Academia andIndustry. SEC 2011. IFIP Advances in Information and Communication Technology,vol 354. Springer, Berlin, Heidelberg. (2011)

  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

    (Third Party Funds Group – Overall project)

    Term: March 1, 2023 - February 28, 2026
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
    URL: https://www.iis.fraunhofer.de/de/ff/zfp/projekte/KI4D4E.html

    Synchrotron tomography is characterized by extremely brilliant X-rays, which enables almost artifact-free imaging. Furthermore, very high resolution can be achieved by using special X-ray optics, and the special design of synchrotron facilities also allows fast in-situ experiments, i.e. 4D tomography.  The combination of these features enables high-resolution computed tomography on objects where conventional laboratory CT fails. At the same time, however, this also produces enormous amounts of data that are generally unprocessable by end users, pushing even the operators of synchrotrons to their limits.

    The goal of the KI4D4E project is to develop AI-based methods that can be used by end users to process the enormous amounts of data in such 4D CT measurements. This includes improving image quality through artifact reduction, reduction and accessibility of data to end users to help the latter interpret the results.

    The project focuses on the topics of artifact reduction, segmentation and visualization of large 4D data sets. The resulting methods should be applicable to data from both photon and neutron sources.

  • Exploring Brain Mechanics (EBM): Understanding, engineering and exploiting mechanical properties and signals in central nervous system development, physiology and pathology

    (Third Party Funds Group – Overall project)

    Term: January 1, 2023 - December 31, 2026
    Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)

    Thecentral nervous system (CNS) is our most complex organ system. Despite tremendousprogress in our understanding of the biochemical, electrical, and geneticregulation of CNS functioning and malfunctioning, many fundamental processesand diseases are still not fully understood. For example, axon growth patterns inthe developing brain can currently not be well-predicted based solely on thechemical landscape that neurons encounter, several CNS-related diseases cannotbe precisely diagnosed in living patients, and neuronal regeneration can stillnot be promoted after spinal cord injuries.

    Duringmany developmental and pathological processes, neurons and glial cells aremotile. Fundamentally, motion is drivenby forces. Hence, CNS cells mechanicallyinteract with their surrounding tissue. They adhere to neighbouring cells and extracellular matrix using celladhesion molecules, which provide friction, and generate forces usingcytoskeletal proteins.  These forces aretransmitted to the outside world not only to locomote but also to probe themechanical properties of the environment, which has a long overseen huge impacton cell function.

    Onlyrecently, groups of several project leaders in this consortium, and a few other groupsworldwide, have discovered an important contribution of mechanical signalsto regulating CNS cell function. For example, they showed that brain tissuemechanics instructs axon growth and pathfinding in vivo, that mechanicalforces play an important role for cortical folding in the developing humanbrain, that the lack of remyelination in the aged brain is due to an increasein brain stiffness in vivo, and that many neurodegenerative diseases areaccompanied by changes in brain and spinal cord mechanics. These first insights strongly suggest thatmechanics contributes to many other aspects of CNS functioning, and it islikely that chemical and mechanical signals intensely interact at the cellularand tissue levels to regulate many diverse cellular processes.

    The CRC 1540 EBM synergises the expertise of engineers, physicists,biologists, medical researchers, and clinicians in Erlangen to explore mechanicsas an important yet missing puzzle stone in our understanding of CNSdevelopment, homeostasis, and pathology. Our strongly multidisciplinary teamwith unique expertise in CNS mechanics integrates advanced invivo, in vitro, and in silico techniques across time(development, ageing, injury/disease) and length (cell, tissue, organ) scalesto uncover how mechanical forces and mechanical cell and tissue properties,such as stiffness and viscosity, affect CNS function. We especially focus on(A) cerebral, (B) spinal, and (C) cellular mechanics. Invivo and in vitro studies provide a basic understanding ofmechanics-regulated biological and biomedical processes in different regions ofthe CNS. In addition, they help identify key mechano-chemical factors forinclusion in in silico models and provide data for model calibration andvalidation. In silico models, in turn, allow us to test hypotheses without the need of excessive or even inaccessibleexperiments. In addition, they enable the transfer and comparison of mechanics data and findingsacross species and scales. They also empower us to optimise processparameters for the development of in vitro brain tissue-like matricesand in vivo manipulation of mechanical signals, and, eventually, pavethe way for personalised clinical predictions.

    Insummary, we exploit mechanics-based approaches to advance ourunderstanding of CNS function and to provide the foundation for futureimprovement of diagnosis and treatment of neurological disorders.

  • AI-refined thermo-hydraulic model for the improvement of the efficiency and quality of water supply

    (Third Party Funds Single)

    Term: November 1, 2023 - October 31, 2026
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)

    The United Nations' goals for sustainable development have made improving quality of life and access to clean drinking water a political priority. However, in recent decades, the water cycle in Bavaria has also been significantly affected by climate change. Two important aspects of daily drinking water supply and distribution are the assurance of water quality and the increase in usage efficiency. To enhance the resilience and capacity of the water supply in general, numerical simulation, data integration, and artificial intelligence (AI) are necessary. In this project, we aim to develop an AI-refined temperature-hydraulic model using heterogeneous data sources from a Bavarian water supply network. Hybrid AI methods are employed to model the complex relationship between water and soil temperature. The resulting model will serve as the basis for various real applications such as leak detection, anomaly recognition, and monitoring of drinking water quality, with the overarching goal of increasing the efficiency and quality of the water supply while simultaneously contributing to the containment of the impact of climate change on drinking water supply

  • Maschinelles Lernen und Datenanalyse für heterogene, artübergreifende Daten (X02)

    (Third Party Funds Group – Sub project)

    Overall project: SFB 1540: Erforschung der Mechanik des Gehirns (EBM): Verständnis, Engineering und Nutzung mechanischer Eigenschaften und Signale in der Entwicklung, Physiologie und Pathologie des zentralen Nervensystems
    Term: January 1, 2023 - December 31, 2026
    Funding source: DFG / Sonderforschungsbereich (SFB)

    X02 nutzt die in EBM erzeugten Bilddaten und mechanischen Messungen, um Deep Learning-Methoden zu entwickeln, die Wissen über Spezies hinweg transferieren. In silico und in vitro Analysen werden deutlich spezifischere Daten liefern als in vivo Experimente, insbesondere für menschliches Gewebe. Um hier Erkenntnisse aus datenreichen Experimenten zu nutzen, werden wir Transfer Learning-Algorithmen für heterogene Daten entwickeln. So kann maschinelles Lernen auch unter stark datenlimitierten Bedingungen nutzbar gemacht werden. Ziel ist es, ein holistisches Verständnis von Bilddaten und mechanischen Messungen über Artgrenzen hinweg zu ermöglichen.

2022

  • International Doctoral Program: Measuring and Modelling Mountain glaciers and ice caps in a Changing Climate (M³OCCA)

    (Third Party Funds Single)

    Term: June 1, 2022 - May 31, 2026
    Funding source: Elitenetzwerk Bayern

    Mountain glaciers and ice caps outside the large ice sheets of Greenland and Antarctica contribute about 41% to the global sea level rise between 1901 to 2018 (IPCC 2021). While the Arctic ice masses are and will remain the main contributors to sea level rise, glacier ice in other mountain regions can be critical for water supply (e.g. irrigation, energy generation, drinking water, but also river transport during dry periods). Furthermore, retreating glaciers also can cause risks and hazards by floods, landslides and rock falls in recently ice-free areas. As a consequence, the Intergovernmental Panel of Climate Change (IPCC) dedicates special attention to the cryosphere (IPCC 2019; IPCC 2021). WMO and UN have defined Essential Climate Variables (ECV) for assessing the status of the cryosphere and its changes. These ECVs should be measured regularly on large scale and are essential to constrain subsequent modelling efforts and predictions.
    The proposed International Doctorate Program (IDP) “Measuring and Modelling Mountain glaciers and ice caps in a Changing ClimAte (M3OCCA)” will substantially contribute to improving our observation and measurement capabilities by creating a unique inter- and transdisciplinary research platform. We will address main uncertainties of current measurements of the cryosphere by developing new instruments and future analysis techniques as well as by considerably advancing geophysical models in glaciology and natural hazard research. The IDP will have a strong component of evolving techniques in the field of deep learning and artificial intelligence (AI) as the data flow from Earth Observation (EO) into modelling increases exponentially. IDP M3OCCA will become the primary focal point for mountain glacier research in Germany and educate emerging
    talents with an interdisciplinary vision as well as excellent technical and soft skills. Within the IDP we combine cutting edge technologies with climate research. We will develop future technologies and transfer knowledge from other disciplines into climate and glacier research to place Bavaria at the forefront in the field of mountain cryosphere research. IDP M3OCCA fully fits into FAU strategic goals and it will leverage on Bavaria’s existing long-term commitment via the super test site Vernagtferner in the Ötztal Alps run by Bavarian Academy of Sciences (BAdW). In addition, we cooperate with the University of Innsbruck and its long-term observatory at Hintereisferner. At those super test sites, we will perform joint measurements, equipment tests, flight campaigns and cross-disciplinary trainings and exercises for our doctoral researchers. We leverage on existing
    instrumentation, measurements and time series. Each of the nine doctoral candidates will be guided by interdisciplinary, international teams comprising university professors, senior scientists and emerging talents from the participating universities and external research organisations.

  • Temporally resolved 3-D retinal blood flow quantification using advanced motion correction and signal reconstruction in optical coherence tomography angiography

    (Third Party Funds Single)

    Term: since November 15, 2022
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)

    Die optische Kohärenztomographie (OCT) erzeugt volumetrische 3-D-Bilder von Gewebe mit Mikrometerauflösung, indem sie einen Laserstrahl zum Scannen verwendet und die Amplitude und Zeitverzögerung von zurückgestreutem Licht misst. Die OCT hat einen großen Einfluss auf die Augenheilkunde und wurde zu einer Standard-Bildgebungsmodalität für die Diagnose, die Überwachung des Krankheitsverlaufs und das Ansprechen auf die Behandlung sowie für die Untersuchung der Pathogenese von Krankheiten wie diabetischer Retinopathie, altersbedingter Makuladegeneration und Glaukom. Die jüngste Entwicklung der OCT-Angiographie (OCTA) hat die grundlegende und klinische Forschung dramatisch beschleunigt. OCTA führt eine tiefenaufgelöste (3-D) Bildgebung der retinalen Mikrovaskulatur durch, indem es wiederholt die gleiche Netzhautposition abbildet und den Bewegungskontrast von sich bewegenden Blutzellen erkennt. Im Vergleich zu herkömmlichen Ansätzen, die auf injizierten Kontrastmitteln basieren, hat OCTA den Vorteil, dass es nicht invasiv ist, sodass die Bildgebung bei jedem Patientenbesuch durchgeführt werden kann, was Längsschnittstudien ermöglicht. Allerdings hat OCTA auch einige Einschränkungen. Da eine wiederholte Bildgebung erforderlich ist, um den Blutfluss zu erkennen, sind die Aufnahmezeiten lang und die Daten können durch Augenbewegungen und Bildartefakte verzerrt werden, was eine quantitative Längsschnittanalyse erschwert. OCTA-Algorithmen können das Vorhandensein eines Blutflusses erkennen, sind jedoch nur begrenzt in der Lage, subtile Veränderungen des Flusses aufzulösen, die frühe Anzeichen einer Krankheit sein können. Zeitliche Schwankungen des Flusses, die durch den Herzzyklus oder die funktionelle Reaktion der Netzhaut verursacht werden, sind schwer zu untersuchen. Wir schlagen vor, ein neues Framework für OCTA zu entwickeln, das eine Bewegungskorrektur auf Kapillarebene ermöglicht, Blutflussgeschwindigkeiten differenziert und eine Analyse auf mehreren Zeitskalen ermöglicht (4-D OCTA). Die Fähigkeit, über die Visualisierung der Mikrovaskulatur hinauszugehen und den Fluss und seine zeitlichen Schwankungen zu beurteilen, ermöglicht die Beurteilung subtiler Beeinträchtigungen der mikrovaskulären Perfusion sowie des Herzzyklus und der Reaktion auf funktionelle Stimulation. In Kombination mit der vaskulären strukturellen Bildgebung versprechen diese Fortschritte, neue Krankheitsmarker in früheren Krankheitsstadien bereitzustellen, eine genauere Messung des Krankheitsverlaufs und des Ansprechens auf die Therapie in pharmazeutischen Studien zu ermöglichen und zur Aufklärung der Pathogenese bei Netzhauterkrankungen beizutragen.

2021

  • ODEUROPA: Negotiating Olfactory and Sensory Experiences in Cultural Heritage Practice and Research

    (Third Party Funds Group – Sub project)

    Overall project: ODEUROPA
    Term: January 1, 2021 - December 31, 2022
    Funding source: EU - 8. Rahmenprogramm - Horizon 2020
    URL: https://odeuropa.eu/

    Our senses are gateways to the past. Although museums are slowly discovering the power of multi-sensory presentations, we lack the scientific standards, tools and data to identify, consolidate, and promote the wide-ranging role of scents and smelling in our cultural heritage. In recent years, European cultural heritage institutions have invested heavily in large-scale digitization. A wealth of object, text and image data that can be analysed using computer science techniques now exists. However, the potential olfactory descriptions, experiences, and memories that they contain remain unexplored. We recognize this as both a challenge and an opportunity. Odeuropa will apply state-of-the-art AI techniques to text and image datasets that span four centuries of European history. It will identify the vocabularies, spaces, events, practices, and emotions associated with smells and smelling. The project will curate this multi-modal information, following semantic web standards, and store the enriched data in a ‘European Olfactory Knowledge Graph’ (EOKG). We will use this data to identify ‘storylines’, informed by cultural history and heritage research, and share these with different audiences in different formats: through demonstrators, an online catalogue, toolkits and training documentation describing best-practices in olfactory museology. New, evidence-based methodologies will quantify the impact of multisensory visitor engagement. This data will support the implementation of policy recommendations for recognising, promoting, presenting and digitally preserving olfactory heritage. These activities will realize Odeuropa’s main goal: to show that smells and smelling are important and viable means for consolidating and promoting Europe’s tangible and intangible cultural heritage.

  • SmartCT - Erforschung und Entwicklung von Methoden der Künstlichen Intelligenz für ein autonomes Roboter-CT System zur 3D-Digitalisierung beliebiger Objekte

    (Third Party Funds Group – Overall project)

    Term: June 1, 2021 - May 31, 2024
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
    URL: https://www.pinterguss.de/forschung-entwicklung/smart-ct.html

    In Vorhaben SmartCT sollen KI-Methoden entwickelt und angewendet werden, die Roboter-CT Systemen ermöglicht, selbstständig, also autonom, die äußeren und inneren Strukturen beliebiger Objekte zu digitalisieren. Diese so erzeugten Daten stellen die Basis von neuartigen, innovativen und datengetriebenen Geschäftsmodellen in vielen Bereichen wie Produktentwicklung, Produktion, Handel, Instandhaltung, Sicherheit und Recycling dar.
    Roboter-CT Systeme können beliebige Objekte (Fahrzeugkomponenten, Flugzeugflügel, Batterie-zellen, Versandpaket, etc.) zerstörungsfrei digitalisieren. Diese Systeme sind jedoch hoch komplex und deshalb bisher nur mit großem zeitlichen Aufwand von Experten bedien- und einsetzbar. Roboter-CT Systeme werden deshalb aktuell nur bei großen Unternehmen eingesetzt. Mit Hilfe der in SmartCT entwickelten KI-Methoden soll es möglich werden, dass beliebige Objekte effizient und wirtschaftlich attraktiv digitalisiert werden können, so dass auch kleinere und mittlere Unternehmen die Vorteile dieser Technologie vollständig zugänglich gemacht werden kann. Zugleich wird mit diesem Vorhaben die Akzeptanz roboterbasierter CT-Systeme in der Industrie nachhaltig erhöht.

  • UtilityTwin

    (Third Party Funds Group – Overall project)

    Term: September 1, 2021 - August 31, 2024
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)

    In the UtilityTwin research project, an intelligent digital twin for any energy or water supply network is to be researched and developed on the basis of adaptive high-resolution sensor data (down to the sub-second range) and machine learning techniques. Overall, the technology concepts BigData and AI are to be combined in an innovative way in this research project in order to make positive contributions to the implementation of the energy transition and to counteract climate change.

2020

  • Bereitstellung einer Infrastruktur zur Nutzung für die Ausbildung Studierender auf einem z/OS Betriebssystem der Fa. IBM

    (FAU Funds)

    Term: April 2, 2020 - March 31, 2025
    Funding source: Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Automatic exposure control (AEC) for CT based on neural network-driven patient-specific real-time assessment of dose distributions and minimization of the effective dose

    (Third Party Funds Single)

    Term: April 1, 2020 - March 31, 2023
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Modern diagnostic CT systems comprise a variety of measures to keep patient dose at a minimum. Of particular importance is the tube current modulation (TCM) technique that automatically adapts the tube current for each projection in a way to minimize the tube current time product (mAs-product) for a given image quality. This can also be regarded as maximizing the image quality for a given mAs-product. TCM performs a modulation depending on the angular position of the x-ray tube and depending on the z-position of the scan. Measures related to TCM are the automated choice of the mean tube current and of the optimal tube voltage. These three dose reduction methods are also known under the term automatic exposure control (AEC).As of today, however, the AEC does not minimize the actual patient dose and thereby the actual patient risk. It rather minimizes surrogates thereof. The surrogate of TCM is the mAs-product. The surrogate used to automatically select the tube voltage is the CTDI value or the dose length product (DLP). A direct minimization of the weighted summed organ dose values and thereby the patient risk is currently not practicable due to a) the very high computation times of the Monte Carlo dose calculation algorithms and b) due to the lack of a reliable segmentation of the radiation sensitive and risk-relevant organs.We therefore plan to use artificial neural networks to solve the above-mentioned problems and to realize a new AEC which is capable of directly minimizing patient dose and risk instead of using surrogate parameters. A first neural net will convert the patient topogram(s) into a coarse estimate of the CT volume. In cases where only a single topogram is available information of the table height will be used for this estimation. A second neural net will segment the relevant organs. We can here partially use prior work of a previous DFG project (KA 1678/20, LE 2763/2, MA 4898/5). A third network will use further scan parameters (table increment, pitch value, rotation time, collimation, tube voltage, …) to compute the expected dose distribution per projection. This, together with the segmentation of the organs, shall be used to compute the effective dose (or risk) or the patient per projection. A minimization algorithm will then find the optimal tube current curve that minimizes patient risk at a given image quality or that maximizes image quality at a given patient risk.To evaluate our deep AEC algorithm diagnostic CT data will be collected. The data will be retrospectively converted to the desired tube current curve by adding noise to the rawdata followed by another reconstruction. Experienced radiologists will then perform a blinded study where they read images produced without AEC, with the conventional AEC as of today, and with our new deep AEC algorithm.
  • Entwicklung eines Leitfadens zur dreidimensionalen zerstörungsfreien Erfassung von Manuskripten

    (Third Party Funds Single)

    Term: since November 1, 2020
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    URL: https://gepris.dfg.de/gepris/projekt/433501541
  • Förderantrag zur Entwicklung des Kurses „Deep Learning for beginners“

    (Third Party Funds Single)

    Term: September 1, 2020 - August 31, 2021
    Funding source: Virtuelle Hochschule Bayern
  • Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie und Rückfallvermeidung am Beispiel von Brustkrebs

    (Third Party Funds Single)

    Term: October 1, 2020 - September 30, 2024
    Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)

    Breast cancer is one of the leading causes of death in the field of oncology in Germany. For the successful care and treatment of patients with breast cancer, a high level of information for those affected is essential in order to achieve a high level of compliance with the established structures and therapies. On the one hand, the digitalisation of medicine offers the opportunity to develop new technologies that increase the efficiency of medical care. On the other hand, it can also strengthen patient compliance by improving information and patient integration through electronic health applications. Thus, a reduction in mortality and an improvement in quality of life can be achieved. Within the framework of this project, digital health programmes are going to be created that support and complement health care. The project aims to provide better and faster access to new diagnostic and therapeutic procedures in mainstream oncology care, to implement eHealth models for more efficient and effective cancer care, and to improve capacity for patients in oncologcal therapy in times of crisis (such as the SARS-CoV-2 pandemic). The Chair of Health Management is conducting the health economic evaluation and analysing the extent to which digitalisation can contribute to a reduction in the costs of treatment and care as well as to an improvement in the quality of life of breast cancer patients.

  • Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie undRückfallvermeidung am Beispiel von Brustkrebs - DigiOnko

    (Third Party Funds Single)

    Term: October 1, 2020 - September 30, 2024
    Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
  • Intelligente MR-Diagnostik der Leber durch Verknüpfung modell- und datengetriebener Verfahren

    (Third Party Funds Single)

    Term: April 1, 2020 - March 31, 2023
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
  • Intelligent MR Diagnosis of the Liver by Linking Model and Data-driven Processes (iDELIVER)

    (Third Party Funds Single)

    Term: August 3, 2020 - March 31, 2023
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)

    The project examines the use and further development of machine learning methods for MR image reconstruction and for the classification of liver lesions. Based on a comparison model and data-driven image reconstruction methods, these are to be systematically linked in order to enable high acceleration without sacrificing diagnostic value. In addition to the design of suitable networks, research should also be carried out to determine whether metadata (e.g. age of the patient) can be incorporated into the reconstruction. Furthermore, suitable classification algorithms on an image basis are to be developed and the potential of direct classification on the raw data is to be explored. In the long term, intelligent MR diagnostics can significantly increase the efficiency of use of MR hardware, guarantee better patient care and set new impulses in medical technology.

  • Molecular Assessment of Signatures ChAracterizing the Remission of Arthritis

    (Third Party Funds Single)

    Term: April 1, 2020 - September 30, 2022
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)

    MASCARA zielt auf eine detaillierte, molekulare Charakterisierung der Remission bei Arthritis ab. Das Projekt basiert auf der kombinierten klinischen und technischen Erfahrung von Rheumatologen, Radiologen, Medizinphysikern, Nuklearmedizinern, Gastroenterologen, grundlagenwissenschaftlichen Biologen und Informatikern und verbindet fünf akademische Fachzentren in Deutschland. Das Projekt adressiert 1) den Umstand der zunehmenden Zahl von Arthritis Patienten in Remission, 2) die Herausforderungen, eine effektive Unterdrückung der Entzündung von einer Heilung zu unterscheiden und 3) das begrenzte Wissen über die Gewebeveränderungen in den Gelenken von Patienten mit Arthritis. MASCARA wird auf der Grundlage vorläufiger Daten vier wichtige mechanistische Bereiche (immunstoffwechselbedingte Veränderungen, mesenchymale Gewebereaktionen, residente Immunzellen und Schutzfunktion des Darms) untersuchen, die gemeinsam den molekularen Zustand der Remission bestimmen. Das Projekt zielt auf die Sammlung von Synovialbiopsien und die anschließende Gewebeanalyse bei Patienten mit aktiver Arthritis und Patienten in Remission ab. Die Gewebeanalysen umfassen (Einzelzell)-mRNA-Sequenzierung, Massenzytometrie sowie die Messung von Immunmetaboliten und werden durch molekulare Bildgebungsverfahren wie CEST-MRT und FAPI-PET ergänzt. Sämtliche Daten, die in dem Vorhaben generiert werden, werden in einem bereits bestehenden Datenbanksystem mit den Daten der anderen Partner zusammengeführt und gespeichert. Das Zusammenführen der Daten soll – mit Hilfe von maschinellem Lernen – krankheitsspezifische und mit der Krankheitsaktivität verbundene Mustermatrizen identifizieren.

  • Entwicklung eines Leitfadens zur dreidimensionalen zerstörungsfreien Erfassung von Manuskripten

    (Third Party Funds Single)

    Term: May 1, 2020 - April 30, 2022
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    URL: https://gepris.dfg.de/gepris/projekt/433501541?context=projekt&task=showDetail&id=433501541&
    In the course of massive digitization, a large part of libraries’ archived documents are currently being converted into electronic formats. However, the digitization is also reaching its limits. Scanning robots cannot digitize documents whose condition due to natural ageing or external influences prohibit a conventional, optical based processing. Our own preliminary work has shown that the three imaging techniques X-ray Computed Tomography, Phase Contrast X-ray Computed Tomography and Terahertz Imaging are suitable for providing non-invasive insights into such documents, allow the acquisition of digital imaging information and are capable to re-enable an efficient automated process to digitalize cultural heritage documents.This research project is the first to develop a concrete digitization strategy or method for such documents. This structured evaluation will be based on a quality value that allows statements to be made about the expected result of digitization with one of the mentioned modalities for certain historical materials. From this, the most suitable imaging procedure can be determined. Based on these findings, a guideline for the digitization of fragile documents will be developed to predict the quality, feasibility and possible damage before a scan. In addition, algorithms will be developed that virtually process the generated data and make it readable for the human eye. Three concrete goals will be pursued to carry out the research project. By evaluating the modalities for selected historical materials, the most appropriate procedure for a specific document should then be identified. At the end of the project, a guide will be made available and the possibilities of each modality will be demonstrated by specifying material combinations and relevant parameters. It will be possible to test the variation of the recording parameters and to display exemplary results using the generated database. This also makes it possible to calculate a quality value. The basis for such a guide is the evaluation of the three modalities for relevant materials. For this purpose, realistic test specimen are produced. Both the scan quality and resolution as well as possible damage to the document must be considered. The guide will then be used to identify the most suitable procedure for a specific document. This statement is based on the mentioned quality value, which will also be used to predict the optimal digitization modality and the quality for an unknown document.The evaluation of several modalities as well as the development of algorithms are to be seen as central challenges of the research project. It will be possible to store endangered holdings in a digital format without destroying their structure through manual intervention. In the second funding phase, a multi-modal solution should be investigated in which disadvantages and limitations of individual modalities will be compensated by combining several modalities.
  • Verbesserte Dual Energy Bildgebung mittels Maschinellem Lernen

    (Third Party Funds Single)

    Term: April 1, 2020 - December 31, 2020
    Funding source: Europäische Union (EU)

    The project aims to develop novel and innovative methods to improve visualisation and use of dual energy CT (DECT) images. Compared to conventional single energy CT (SECT) scans, DECT contains a significant amount of additional quantitative information that enables tissue characterization far beyond what is possible with SECT, including material decomposition for quantification and labelling of specific materials within tissues, creation of reconstructions at different predicted energy levels, and quantitative spectral tissue characterization for tissue analysis. However, despite the many potential advantages of DECT, applications remain limited and in specizlized clinical settings. Some reasons are that many applications are specific for the organ under investigation, require additional, manual processing or calibration, and not easily manipulated using standard interactive contrast visualisation windows available in clinical viewing stations. This is a significant disadvantage compared to conventional SECT.
    In this project, we propose to develop new strategies to fuse and display the additional DECT information on a single contrast scale such that it can be visualised with the same interactive tools that radiologists are used to in their clinical routine. We will investigate non-linear manifold learning techniques like Laplacian Eigenmaps and the Sammon Mapping. Both allow extension using AI-based techniques like the newly developed user loss that allows to integrate user's opinions using forced choice experiments. This will allow a novel image contrast that will be compatible with interactive window and level functions that are rourintely used by radiologists. Furthermore, we aim at additional developments that will use deep neural networks to approximate the non-linear mapping function and to generate reconstructions that capture and display tissue specific spectral characteristics in a readily and universally useable manner for enhancing diagnostic value.

2019

  • ICONOGRAPHICS: Computational Understanding of Iconography and Narration in Visual Cultural Heritage

    (FAU Funds)

    Term: April 1, 2019 - March 31, 2021

    The interdisciplinary research project Iconographics is dedicated to innovative possibilities of digital image recognition for the arts and humanities. While computer vision is already often able to identify individual objects or specific artistic styles in images, the project is confronted with the open problem of also opening up the more complex image structures and contexts digitally. On the basis of a close interdisciplinary collaboration between Classical Archaeology, Christian Archaeology, Art History and the Computer Sciences, as well as joint theoretical and methodological reflection, a large number of multi-layered visual works will be analyzed, compared and contextualized. The aim is to make the complex compositional, narrative and semantic structures of these images tangible for computer vision.

    Iconography and Narratology are identified as a challenging research questions for all subjects of the project. The iconography will be interpreted in its plot, temporality, and narrative logic. Due to its complex cultural structure; we selected four important scenes:

    1. The Annunciation of the Lord
    2. The Adoration of the Magi
    3. The Baptism of Christ
    4. Noli me tangere (Do not touch me)
  • Improving multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information

    (Non-FAU Project)

    Term: April 1, 2019 - April 30, 2022

    This project aims to improve multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information. Such improvements include noise reduction and artifact removal from data acquired in SPECT.

  • Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope

    (Third Party Funds Single)

    Term: April 1, 2019 - March 31, 2025
    Funding source: European Research Council (ERC)
    URL: https://cordis.europa.eu/project/id/810316

    Due to Europe's ageing society, there has been a dramatic increase in the occurrence of osteoporosis (OP) and related diseases. Sufferers have an impaired quality of life, and there is a considerable cost to society associated with the consequent loss of productivity and injuries. The current understanding of this disease needs to be revolutionized, but study has been hampered by a lack of means to properly characterize bone structure, remodeling dynamics and vascular activity. This project, 4D nanoSCOPE, will develop tools and techniques to permit time-resolved imaging and characterization of bone in three spatial dimensions (both in vitro and in vivo), thereby permitting monitoring of bone remodeling and revolutionizing the understanding of bone morphology and its function.

    To advance the field, in vivo high-resolution studies of living bone are essential, but existing techniques are not capable of this. By combining state-of-the art image processing software with innovative 'precision learning' software methods to compensate for artefacts (due e.g. to the subject breathing or twitching), and innovative X-ray microscope hardware which together will greatly speed up image acquisition (aim is a factor of 100), the project will enable in vivo X-ray microscopy studies of small animals (mice) for the first time. The time series of three-dimensional X-ray images will be complemented by correlative microscopy and spectroscopy techniques (with new software) to thoroughly characterize (serial) bone sections ex vivo.

    The resulting three-dimensional datasets combining structure, chemical composition, transport velocities and local strength will be used by the PIs and international collaborators to study the dynamics of bone microstructure. This will be the first time that this has been possible in living creatures, enabling an assessment of the effects on bone of age, hormones, inflammation and treatment.

  • Advancing osteoporosis medicine by observing bone microstructure and remodelling using a fourdimensional nanoscope

    (Third Party Funds Group – Sub project)

    Overall project: Advancing osteoporosis medicine by observing bone microstructure and remodelling using a fourdimensional nanoscope
    Term: April 1, 2019 - March 31, 2025
    Funding source: EU - 8. Rahmenprogramm - Horizon 2020
  • Artificial Intelligence for Reinventing European Healthcare

    (Third Party Funds Group – Sub project)

    Overall project: Artificial Intelligence for Reinventing European Healthcare
    Term: January 1, 2019 - December 31, 2019
    Funding source: EU - 8. Rahmenprogramm - Horizon 2020
  • Big Data of the Past for the Future of Europe

    (Third Party Funds Group – Sub project)

    Overall project: TIME MACHINE : BIG DATA OF THE PAST FOR THE FUTURE OF EUROPE
    Term: March 1, 2019 - February 29, 2020
    Funding source: EU - 8. Rahmenprogramm - Horizon 2020
  • Deep-Learning basierte Segmentierung und Landmarkendetektion auf Röntgenbildern für unfallchirurgische Eingriffe

    (Third Party Funds Single)

    Term: since May 6, 2019
    Funding source: Siemens AG
  • Kombinierte Iterative Rekonstruktion und Bewegungskompensation für die Optische Kohärenz Tomographie-Angiographie

    (Third Party Funds Single)

    Term: June 1, 2019 - May 31, 2021
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  • Kommunikation und Sprache im Reich. Die Nürnberger Briefbücher im 15. Jahrhunddert: Automatische Handschriftenerkennung - historische und sprachwissenschaftliche Analyse

    (Third Party Funds Single)

    Term: October 1, 2019 - September 30, 2022
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  • Kommunikation und Sprache im Reich. Die Nürnberger Briefbücher im 15. Jahrhundert: Automatische Handschriftenerkennung - historische und sprachwissenschaftliche Analyse.

    (Third Party Funds Single)

    Term: October 1, 2019 - September 30, 2022
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  • PPP Brasilien 2019

    (Third Party Funds Single)

    Term: January 1, 2019 - December 31, 2020
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)
  • Deep Learning based Noise Reduction for Hearing Aids

    (Third Party Funds Single)

    Term: February 1, 2019 - January 31, 2023
    Funding source: Industrie
     

    Reduction of unwanted environmental noises is an important feature of today’s hearing aids, which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. Due to the large number of different background noises in daily situations, it is hard to heuristically cover the complete solution space of noise reduction schemes. Deep learning-based algorithms pose a possible solution to this dilemma, however, they sometimes lack robustness and applicability in the strict context of hearing aids.
    In this project we investigate several deep learning.based methods for noise reduction under the constraints of modern hearing aids. This involves a low latency processing as well as the employing a hearing instrument-grade filter bank. Another important aim is the robustness of the developed methods. Therefore, the methods will be applied to real-world noise signals recorded with hearing instruments.

  • Tapping the potential of Earth Observations

    (FAU Funds)

    Term: April 1, 2019 - March 31, 2022

2018

  • Automatic Intraoperative Tracking for Workflow and Dose Monitoring in X-Ray-based Minimally Invasive Surgeries

    (Third Party Funds Single)

    Term: June 1, 2018 - May 31, 2021
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)

    The goal of this project is the investigation of multimodal methods for the evaluation of interventional workflows in the operation room. This topic will be researched in an international project context with partners in Germany and in Brazil (UNISINOS in Porto Alegre). Methods will be developed to analyze the processes in an OR based on signals from body-worn sensors, cameras and other modalities like X-ray images recorded during the surgeries. For data analysis, techniques from the field of computer vision, machine learning and pattern recognition will be applied. The system will be integrated in a way that body-worn sensors developed by Portabiles as well as angiography systems produced by Siemens Healthcare can be included alongside.

  • Automatisiertes Intraoperatives Tracking zur Ablauf- und Dosisüberwachung in RöntgengestütztenMinimalinvasiven Eingriffen

    (Third Party Funds Group – Sub project)

    Overall project: Automatisiertes Intraoperatives Tracking zur Ablauf- und Dosisüberwachung in RöntgengestütztenMinimalinvasiven Eingriffen
    Term: June 1, 2018 - May 31, 2021
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
  • Deep Learning Applied to Animal Linguistics

    (FAU Funds)

    Term: April 1, 2018 - April 1, 2022
    Deep Learning Applied to Animal Linguistics in particular the analysis of underwater audio recordings of marine animals (killer whales):

    For marine biologists, the interpretation and understanding of underwater audio recordings is essential. Based on such recordings, possible conclusions about behaviour, communication and social interactions of marine animals can be made. Despite a large number of biological studies on the subject of orca vocalizations, it is still difficult to recognize a structure or semantic/syntactic significance of orca signals in order to be able to derive any language and/or behavioral patterns. Due to a lack of techniques and computational tools, hundreds of hours of underwater recordings are still manually verified by marine biologists in order to detect potential orca vocalizations. In a post process these identified orca signals are analyzed and categorized. One of the main goals is to provide a robust and automatic method which is able to automatically detect orca calls within underwater audio recordings. A robust detection of orca signals is the baseline for any further and deeper analysis. Call type identification and classification based on pre-segmented signals can be used in order to derive semantic and syntactic patterns. In connection with the associated situational video recordings and behaviour descriptions (provided by several researchers on site) can provide potential information about communication (kind of a language model) and behaviors (e.g. hunting, socializing). Furthermore, orca signal detection can be used in conjunction with a localization software in order to provide researchers on the field with a more efficient way of searching the animals as well as individual recognition.

    For more information about the DeepAL project please contact christian.bergler@fau.de.

  • Analysis of Defects on Solar Power Cells

    (Third Party Funds Group – Sub project)

    Overall project: iPV 4.0: Intelligente vernetzte Produktion mittels Prozessrückkopplung entlang des Produktlebenszyklus von Solarmodulen
    Term: August 1, 2018 - July 31, 2021
    Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)

    Over the last decade, a large number of solar power plants have been installed in Germany. To ensure a high performance, it is necessary to detect defects early. Therefore, it is required to control the quality of the solar cells during the production process, as well as to monitor the installed modules. Since manual inspections are expensive, a large degree of automation is required.
    This project aims to develop a new approach to automatically detect and classify defects on solar power cells and to estimate their impact on the performance. Further, big data methods will be applied to identify circumstances that increase the probability of a cell to become defect. As a result, it will be possible to reject cells in the production that have a high likelihood to become defect.

  • Digitalization in clinical settings using graph databases

    (Non-FAU Project)

    Term: since October 1, 2018
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)

    In clinical settings, different data is stored in different systems. These data are very heterogeneous, but still highly interconnected. Graph databases are a good fit for this kind of data: they contain heterogeneous "data nodes" which can be connected to each other. The basic question is now if and how clinical data can be used in a graph database, most importantly how clinical staff can profit from this approach. Possible scenarios are a graphical user interface for clinical staff for easier access to required information or an interface for evaluation and analysis to answer more complex questions. (e.g., "Were there similar patients to this patient? How were they treated?")

  • Entwicklung eines Modellrepositoriums und einer Automatischen Schriftarterkennung für OCR-D

    (Third Party Funds Single)

    Term: July 1, 2018 - December 31, 2019
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  • Critical Catalogue of Luther Portraits (1519-1530)

    (Third Party Funds Group – Overall project)

    Term: June 1, 2018 - May 31, 2021
    Funding source: andere Förderorganisation
    URL: https://www.gnm.de/forschung/projekte/luther-bildnisse/

    Quite a number of portraits of Martin Luther - known as media star of 16th century – can be found in today’s museums and libraries. However, how many portraits indeed exist and which of those are contemporary or actually date from a later period? So far unlike his works, however, the variety of contemporary portraits (painting and print) is neither completely collected nor critically analyzed. Thus, a joint project of the FAU, the Germanisches Nationalmuseum (GNM) in Nuremberg and the Technology Arts Sciences (TH Köln) was initiated. Goal of the interdisciplinary project covering art history, art technology, reformation history and computer science is the creation of a critical catalogue of Luther portraits (1519-1530). In particular, the issues of authenticity, dating of artworks and its historical usage context as well as the potential existence of serial production processes will be investigated.

  • Laboranalyse von Degradationsmechanismen unter beschleunigter Alterung und Entwicklung geeigneter feldtauglicher bildgebender Detektionsverfahren und Entwicklung und Evaluation eines Algorithmus zur Fehlerdetektion und Prognostizierung der Ausfallwahrscheinlichkeit

    (Third Party Funds Group – Overall project)

    Term: August 1, 2018 - July 31, 2021
    Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
  • Medical Image Processing for Interventional Applications

    (Third Party Funds Single)

    Term: January 1, 2018 - December 31, 2018
    Funding source: Virtuelle Hochschule Bayern
  • Moderner Zugang zu historischen Quellen

    (Third Party Funds Group – Sub project)

    Overall project: Moderner Zugang zu historischen Quellen
    Term: March 1, 2018 - February 28, 2021
    Funding source: andere Förderorganisation
  • Radiologische und Genomische Datenanalyse zur Verbesserung der Brustkrebstherapie

    (Third Party Funds Single)

    Term: January 1, 2018 - December 31, 2019
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
  • Similarity learning for art analysis

    (Third Party Funds Group – Sub project)

    Overall project: Critical Catalogue of Luther Portraits (1519-1530)
    Term: June 1, 2018 - February 28, 2021
    Funding source: andere Förderorganisation
    URL: https://www.gnm.de/forschung/forschungsprojekte/luther-bildnisse/

    The analysis of the similarity of portraits is an important issue for many sciences such as art history or digital humanities, as for instance it might give hints concerning serial production processes, authenticity or temporal and contextual classification of the artworks.
    In the project, first algorithms will be developed for cross-genre and multi-modal registration of portraits to overlay digitized paintings and prints as well as paintings acquired with different imaging systems such as visual light photography and infrared reflectography. Then, methods will be developed to objectively analyze the portraits according to their similarity.
    This project is part of a joint project of the FAU, the Germanisches Nationalmuseum (GNM) in Nuremberg and the Technology Arts Sciences (TH Köln) in Cologne. Goal of the interdisciplinary project covering art history, art technology, reformation history and computer science is the creation of a critical catalogue of Luther portraits (1519-1530).

2017

  • 3-D Multi-Contrast CINE Cardiac Magnetic Resonance Imaging

    (Non-FAU Project)

    Term: October 1, 2017 - September 30, 2020
  • Deep Learning for Multi-modal Cardiac MR Image Analysis and Quantification

    (Third Party Funds Single)

    Term: January 1, 2017 - May 1, 2020
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)

    Cardiovascular diseases (CVDs) and other cardiac pathologies are the leading cause of death in Europe and the USA. Timely diagnosis and post-treatment follow-ups are imperative for improving survival rates and delivering high-quality patient care. These steps rely heavily on numerous cardiac imaging modalities, which include CT (computerized tomography), coronary angiography and cardiac MRI. Cardiac MRI is a non-invasive imaging modality used to detect and monitor cardiovascular diseases. Consequently, quantitative assessment and analysis of cardiac images is vital for diagnosis and devising suitable treatments. The reliability of quantitative metrics that characterize cardiac functions such as, myocardial deformation and ventricular ejection fraction, depends heavily on the precision of the heart chamber segmentation and quantification. In this project, we aim to investigate deep learning methods to improve the diagnosis and prognosis for CVDs,

  • Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

    (FAU Funds)

    Term: March 1, 2017 - June 30, 2020

    Breast cancer is the leading cause of cancer related deaths in women, the second most common cancer worldwide. The development and progression of breast cancer is a dynamic biological and evolutionary process. It involves a composite organ system, with transcriptome shaped by gene aberrations, epigenetic changes, the cellular biological context, and environmental influences. Breast cancer growth and response to treatment has a number of characteristics that are specific to the individual patient, for example the response of the immune system and the interaction with the neighboring tissue. The overall complexity of breast cancer is the main cause for the current, unsatisfying understanding of its development and the patient’s therapy response. Although recent precision medicine approaches, including genomic characterization and immunotherapies, have shown clear improvements with regard to prognosis, the right treatment of this disease remains a serious challenge. The vision of the BIG-THERA team is to improve individualized breast cancer diagnostics and therapy, with the ultimate goal of extending the life expectancy of breast cancer sufferers. Our primary contribution in this regard is developing a multi-modal, multi-scale framework for the early diagnosis of the molecular sub-types of breast cancer, in a manner that supplements the clinical diagnostic workflow and enables the early identification of patients compatible with specific immunotherapeutic solutions.

  • Digital Pathology - New Approaches to the Automated Image Analysis of Histologic Slides

    (Own Funds)

    Term: since January 16, 2017

    The pathologist is still the gold standard in the diagnosis of diseases in tissue slides. Due to its human nature, the pathologist is on one side able to flexibly adapt to the high morphological and technical variability of histologic slides but of limited objectivity due to cognitive and visual traps.

    In diverse project we are applying and validating currently available tools and solutions in digital pathology but are also developing new solution in automated image analysis to complement and improve the pathologist especially in areas of quantitative image analysis.

  • Integrative 'Big Data Modeling' for the development of novel therapeutic approaches for breast cancer

    (FAU Funds)

    Term: January 1, 2017 - June 30, 2020

    Brustkrebs ist die häufigste Ursache für den Krebstod bei Frauen, die zweithäufigste Krebsart weltweit und die fünfthäufigste Ursache für krebsbedingte Todesfälle. Die Entwicklung und der Fortschritt von Brustkrebs ist ein dynamischer biologischer und evolutionärer Prozess. Es handelt sich um ein komplexes Organsystem, dessen Transkriptom durch Genaberrationen, epigenetische Veränderungen, dem zellulären biologischen Kontext und Umwelteinflüsse geprägt ist. Das Wachstum von Brustkrebs und die Reaktion auf die Behandlung hat eine Reihe von Eigenschaften, die für den einzelnen Patienten spezifisch sind, zum Beispiel die Reaktion des Immunsystems und die Interaktion mit dem benachbarten Gewebe. Die Gesamtkomplexität des Brustkrebses ist die Hauptursache für das aktuelle, unbefriedigende Verständnis seiner Entwicklung und des Therapieansprechens des Patienten. Obwohl die jüngsten präzisionsmedizinischen Ansätze, einschließlich genomischer Charakterisierung und Immuntherapien, deutliche Verbesserungen in der Prognose gezeigt haben, bleibt die richtige Behandlung dieser Krankheit eine große Herausforderung. Die Vision des BIG-THERA-Teams ist es, die individuelle Diagnose und Therapie von Brustkrebs zu verbessern, mit dem Ziel, die Lebenserwartung dieser Patienten zu verlängern.

    Folgende Ziele hat sich das BIG-THERA-Team gesetzt:

    • die Verfahren zur nicht-invasiven Früherkennung und Therapieverfolgung auf der Grundlage der Magnetresonanztomographie (MRT) zu verbessern
    • das Zusammenspiel zwischen dem Immunsystem und dem Krebswachstum zur Trennung immunologisch unterschiedlicher Brustkrebs-Subtypen für das Immuntherapie-Design aufzuklären
    • neue Strategien für die Immunphänotypisierung von Tumoren mit nanomedizinischen Techniken zu entwickeln
    • ethische Herausforderungen im Zusammenhang mit den neuen Fortschritten in der Brustkrebsforschung zu lösen
    • therapeutische Entscheidungen unter Verwendung von Big-Data Datensätzen und Informationen aus in vitro, in vivo und in silico OMICs Studien, Bildgebung und Modellierung basieren, zu optimieren.
  • Joint Iterative Reconstruction and Motion Compensation for Optical Coherence Tomography Angiography

    (Third Party Funds Single)

    Term: August 1, 2017 - July 31, 2019
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)

    Optical coherence tomography (OCT) is a non-invasive 3-D optical imagingmodality that is a standard of care in ophthalmology [1,2]. Since the introduction of Fourier-domain OCT [3], dramatic increases in imaging speedbecame possible, enabling 3-D volumetric data to be acquired. Typically, aregion of the retina is scanned line by line, where each scanned lineacquires a cross-sectional image or a B-scan. Since B-scans are acquiredin milliseconds, slices extracted along a scan line, or the fast scanaxis, are barely affected by motion. In contrast, slices extractedorthogonally to scan lines, i. e. in slow scan direction, areaffected by various types of eye motion occurring throughout the full,multi-second volume acquisition time. The most relevant types of eyemovements during acquisition are (micro-)saccades, which can introducediscontinuities or gaps between B-scans, and slow drifts, which causesmall, slowly changing distortion [4]. Additional eye motion is caused by pulsatile blood flow,respiration and head motion. Despite ongoing advances in instrumentscanning speed [5,6] typical volume acquisition times havenot decreased. Instead, the additional scanning speed is used for densevolumetric scanning or wider fields of view [7]. OCT angiography (OCTA) [811] multiplies therequired number of scans by at least two, and even more scans are neededto accommodate recent developments in blood flow speed estimation whichare based on multiple interscan times [12,13]. As a consequence,there is an ongoing need for improvement in motion compensation especiallyin pathology [1416].

    We develop novel methods for retrospective motion correction of OCT volume scans of the anterior and posterior eye, and widefield imaging. Our algorithms are clinically usable due to their suitability for patients with limited fixation capabilities and increased amount of motion, due to their fast processing speed, and their high accuracy, both in terms of alignment and motion correction. By merging multiple accurately aligned scans, image quality can be increased substantially, enabling the inspection of novel features.

  • Verbesserte Charakterisierung des Versagensverhaltens von Blechwerkstoffen durch den Einsatz von Mustererkennungsmethoden

    (Third Party Funds Single)

    Term: April 1, 2017 - March 31, 2019
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)

2016

  • Intraoperative brain shift compensation and point-based vascular registration

    (Non-FAU Project)

    Term: May 1, 2016 - October 31, 2019
  • CODE

    (Third Party Funds Single)

    Term: December 1, 2016 - November 30, 2017
    Funding source: Industrie
  • Computer-based motive assessment

    (Own Funds)

    Term: since January 1, 2016

    The standard method of measuring motives -- coding imaginative stories for motivational themes -- places a heavy burden on researchers in terms of the time and the personnel invested in this task. We collaborate with colleagues from lingusitics and computer science on the development of computer-based, automated procedures for assessing implicit motivational needs in written text. To this purpose, we use standard psycholinguistic procedures such as the Lingusitic Inquiry and Word Count software as well as sophisticated pattern recognition approaches.

  • Digital, Semantic and Physical Analysis of Media Integrity

    (Third Party Funds Single)

    Term: May 24, 2016 - May 23, 2017
    Funding source: Industrie
  • Iterative Rekonstruktionsmethoden mit Fokus auf abdominelle MR-Bildgebung

    (Third Party Funds Single)

    Term: December 1, 2016 - April 30, 2017
    Funding source: Siemens AG
  • Medical Image Processing for Diagnostic Applications

    (Third Party Funds Single)

    Term: June 1, 2016 - May 31, 2017
    Funding source: Virtuelle Hochschule Bayern
  • Modelbasierte Röntgenbildgebung

    (Third Party Funds Single)

    Term: February 1, 2016 - January 31, 2019
    Funding source: Siemens AG
  • Nichtrigide Registrierung von 3D DSA mit präoperativen Volumendaten, um intraoperativen Brainshift bei offender Schädel-OP zu korrigieren

    (Third Party Funds Single)

    Term: June 1, 2016 - May 31, 2019
    Funding source: Siemens AG
  • Nutzung von Rohdaten-Redundanzen in der Kegelstrahl-CT

    (Third Party Funds Single)

    Term: May 1, 2016 - April 30, 2019
    Funding source: Siemens AG
  • Predicitve Prevention and personalized Interventional Stroke Therapy

    (Third Party Funds Group – Sub project)

    Overall project: Predicitve Prevention and personalized Interventional Stroke Therapy
    Term: January 1, 2016 - December 31, 2018
    Funding source: EU - 8. Rahmenprogramm - Horizon 2020
  • Quantifizierung der Fett-Säuren-Zusammensetzung in der Leber sowie Optimierung der zugehörigen Akquisitions- und Rekonstruktionstechniken

    (Third Party Funds Single)

    Term: June 15, 2016 - June 14, 2019
    Funding source: Siemens AG
  • Quantitative diagnostic dual energy CT with atlas-based prior knowledge

    (Third Party Funds Single)

    Term: January 1, 2016 - May 31, 2019
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)

                                      During the last decade, dual energy CT (DECT) became widely available in clinical routine. Offered by all major CT manufacturers, with differing hard- and software concepts, the DECT applications are alike: the acquired DECT data are used to conduct two- or multi material decompositions, e.g. to separate iodine and bone from soft tissue or to quantify contrast agent and fat, to classify or characterize tissue, or to increase contrasts (CNR maximization), or to suppress contrasts (artifact reduction). The applications are designed to work with certain organs and the user needs to take care to invoke the correct application and to interpret its output only in the appropriate organ or anatomical region. E.g. interpreting the output of a kidney stone applications in organs other than the kidney will yield a wrong classification. To obtain quantitative results the applications require to set patient-specific parameters. In order to calibrate those the user is asked to place ROIs in predefined anatomical regions. Since this is time-consuming users are often tempted to use the default settings instead of optimizing them. Here, we want to develop a DECT atlas to utilize its anatomical (and functional) information for con-text-sensitive DECT imaging and material decomposition, and to be able to automatically calibrate the open parameters without the need of user interaction. To improve quantification the initial images shall not be reconstructed separately but rather undergo a rawdata-based decomposition before being converted into image domain. A dedicated user interface shall be developed to provide the new context-sensitive DECT information - such as automatically decomposing each organ into different but reasonable basis materials, for example - and to display it to the reader in a convenient way. Similarly, user-placed ROIs shall trigger a context-sensitive statistical evaluation of the ROI's contents and provide it to the user. This will help to quantify the iodine uptake in a tumor or a lesion, to separate it from fat or calcium components, to estimate its blood supply etc. Since the DECT data display the contrast uptake just for a given instance in time and since this contrast depends on patient-specific factors such as the cardiac output, we are planning to normalize the contrast uptake with the help of the dual energy information contained in the atlas. This will minimize inter and intra patient effects and increase the reproducibility. In addition, organ-specific material scores shall be developed that quantify a patient's material composition on an organ by organ basis. The new methods (DECT atlas, material decomposition, ...) shall be tested and evaluated using phantom and patient studies, and shall be optimized accordingly.                             

  • Studie zum Thema "Defektspalten/Defektreihen"

    (Third Party Funds Single)

    Term: August 1, 2016 - January 31, 2017
    Funding source: Industrie
  • Weiterentwicklung in der interferometrischen Röntgenbildgebung

    (Third Party Funds Single)

    Term: July 1, 2016 - June 30, 2019
    Funding source: Siemens AG
  • Workshop "Mobile eye imaging and remote diagnosis based on the captured image"

    (Third Party Funds Single)

    Term: October 10, 2016 - October 14, 2016
    Funding source: Industrie
  • Zusammenarbeit auf dem Gebiet der 3D-Modellierung von Koronararterien

    (Third Party Funds Single)

    Term: June 13, 2016 - December 31, 2017
    Funding source: Siemens AG
  • Zusammenarbeit auf dem Gebiet der Navigationsunterstützung in röhrenförmigen Strukturen

    (Third Party Funds Single)

    Term: June 1, 2016 - May 31, 2019
    Funding source: Siemens AG

2015

  • Auto ASPECTS

    (Third Party Funds Single)

    Term: December 1, 2015 - May 31, 2016
    Funding source: Industrie
  • Bildverbesserung der 4D DSA und Flußquantifizierung mittels 4D DSA

    (Third Party Funds Single)

    Term: June 15, 2015 - June 14, 2017
    Funding source: Siemens AG
  • Helical 3-D X-ray Dark-field Imaging

    (Third Party Funds Single)

    Term: April 1, 2015 - March 31, 2019
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    The dark-field signal of an X-ray phase-contrast system captures the small-angle scattering of microscopic structures. It is acquired using Talbot-Lau interferometer, and a conventional X-ray source and a conventional X-ray detector. Interestingly, the measured intensity of the dark-field signal depends on the orientation of the microstructure. Using algorithms from tomographic image reconstruction, it is possible to recover these structure orientations. The size of the imaged structures can be considerably smaller than the resolution of the used X-ray detector. Hence, it is possible to investigate structural properties of - for example - bones or soft tissue at an unprecedented level of detail.Existing methods for 3-D dark-field reconstruction require sampling in all three spatial dimensions. For practical use, this procedure is infeasible.The goal of the proposed project is to develop a system and a method for 3-D reconstruction of structure orientations based on measurements from a practical imaging trajectory. To this end, we propose to use a helical trajectory, a concept that has been applied in conventional CT imaging with tremendous success. As a result, it will be possible for the first time to compute dark-field volumes from a practically feasible, continuous imaging trajectory. The trajectory does not require multiple rotations of the object or the patient and avoids unnecessarily long path lengths.The project will be conducted in cooperation between the experimental physics and the computer science department. The project is composed of six parts:- A: Development of a 3-D cone-beam scattering projection model- B: Development of reconstruction algorithms for a helical dark-field imaging system- C: Evaluation and optimization of the reconstruction methods towards clinical applications- D: Design of an experimental helical imaging system- E: Setup of the helical imaging system- F: Evaluation and optimization of the system performanceParts A to C will be performed by the computer science department. Parts D to E will be conducted by the experimental physics department.
  • Endovaskuläre Versorgung von Aortenaneurysmen

    (Third Party Funds Single)

    Term: December 1, 2015 - November 30, 2018
    Funding source: Industrie
  • Feature-basierte Bildregistrierung für interventionelle Anwendungen

    (Third Party Funds Single)

    Term: July 1, 2015 - June 30, 2018
    Funding source: Siemens AG
  • Forschungskostenzuschuss Dr. Huang, Xiaolin

    (Third Party Funds Single)

    Term: June 1, 2015 - May 31, 2017
    Funding source: Alexander von Humboldt-Stiftung
  • Kalibrierung von Time-of-Flight Kameras

    (Third Party Funds Single)

    Term: October 1, 2015 - March 31, 2017
    Funding source: Stiftungen
  • Consistency Conditions for Artifact Reduction in Cone-beam CT

    (Third Party Funds Single)

    Term: since January 1, 2015
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Tomographic reconstruction is the enabling technology for a wide range of transmission-based 3D imaging modalities, most notably X-ray Computed Tomography (CT). The first CT scanners built in the 70ies used parallel geometries. In order to speed up acquisition, the systems soon moved to fan-beam geometries and a much faster rotation speed. Today´s CT systems rotate four times per second and use a cone-beam geometry. This is fast enough to cover even complex organ motion such as the beating heart. However, there exists a large class of specialized CT systems that are not able to perform such fast scans. Systems using a flat-panel detector, as they are employed in C-arm angiography systems, on-board imaging systems in radiation therapy, or mobile C-arm systems, face mechanical challenges as they were mainly built to perform 2D imaging. About 15 years ago flat-detector scanners have been enabled to acquire three dimensional data. 3D imaging on these systems, however, is challenging due to their slower acquisition speed between five seconds and one minute and a small field-of-view (FOV) with a diameter of 25 to 40 cm. These drawbacks are related to the scanners´ design as highly specialized modalities. In contrast to other disadvantages of flat panel detectors like increased X-ray scattering and limited dynamic range, they will not be remedied by hardware evolution in the foreseeable future, e.g. faster motion is impossible because of the risk of collisions in the operation room. As a result, Flat-Detector Computed Tomography (FDCT) will continue to be more susceptible to artifacts in the reconstructed image due to motion and truncation. The goal of this project is to extend existing data consistency conditions, which can be practically used for FDCT to remedy intrinsic weaknesses of FDCT imaging, most importantly motion and truncation. Our goal is the practical applicability on clinical data. Thus, the new algorithms will be tested on physical phantom and patient data acquired on real FDCT scanners of our project partners. Our long-term vision behind this project is to find a concise and complete formulation for all redundancies within FDCT projection data for general trajectories and fully exploit them in the reconstruction process. Redundancies are inherent to every FDCT scan done in today´s clinical practice, but they are ignored entirely as a source of information. Data consistency conditions do not require any additional effort during the acquisitions and only little prior knowledge such as the object extent or even no assumptions about the underlying object, unlike for example total variation regularization in iterative reconstruction. Hence, they rely solely on information which is naturally present in the data.
  • Kontrast- und katheterbasierte 3D-/2D-Registrierung

    (Third Party Funds Single)

    Term: January 1, 2015 - September 30, 2015
    Funding source: Siemens AG
  • Segmentierung von MR-Daten in der Herzbildgebung zur Verwendung bei Interventionen an Angiographiegeräten

    (Third Party Funds Single)

    Term: October 1, 2015 - September 30, 2018
    Funding source: Siemens AG
  • Verbesserung Freiraumerkennung/fusion im Grid: Odometrie aus Umgebungssensoren

    (Third Party Funds Single)

    Term: June 12, 2015 - May 31, 2016
    Funding source: Industrie
  • Weight-Bearing Image of the Knee Using A-Arm CT

    (Third Party Funds Single)

    Term: April 1, 2015 - February 28, 2019
    Funding source: National Institutes of Health (NIH)

2014

  • 4D Herzbildgebung

    (Third Party Funds Single)

    Term: February 1, 2014 - January 31, 2018
    Funding source: Siemens AG
  • Automatic classification and image analysis of confocal laser endomicroscopy images

    (Own Funds)

    Term: since October 1, 2014

    The goal of this project is to detect cancerous tissue in confocal lasermicroendoscopy (CLE) images of the oral cavity and the vocal cord. The current treatment of these diseases is a histological analysis of specimen and a surgical resection, which has a rather high long-term survival rate, or radiation therapy with a lower survival rate. An early detection of cancerous tissue could lead to a lowered complication rate for further treatment, as well as a better overall prognosis for patients. Further, an in-vivo diagnosis during operation could narrow down the area for the necessary surgical excision, which is especially beneficial for cancer of the vocal cords.

    For this reason, we are applying methods of pattern recognition to facilitate and support diagnosis. We were able to show that these can be applied with high accuracies on CLE images.

  • Magnetresonanz am Herzen

    (Third Party Funds Single)

    Term: March 1, 2014 - June 30, 2017
    Funding source: Siemens AG

2013

  • Bewegungskompensation für Überlagerungen in der interventionellen C-Bogen Bildgebung

    (Third Party Funds Single)

    Term: June 1, 2013 - November 30, 2016
    Funding source: Siemens AG

2012

  • RTG 1773: Heterogeneous Image Systems, Project C1

    (Third Party Funds Group – Sub project)

    Overall project: GRK 1773: Heterogene Bildsysteme
    Term: October 1, 2012 - March 31, 2017
    Funding source: DFG / Graduiertenkolleg (GRK)
    Especially in aging populations, Osteoarthritis (OA) is one of the leading causes for disability and functional decline of the body. Yet, the causes and progression of OA, particularly in the early stages, remain poorly understood. Current OA imaging measures require long scan times and are logistically challenging. Furthermore they are often insensitive to early changes of the tissue.

    The overarching goal of this project is the development of a novel computed tomography imaging system allowing for an analysis of the knee cartilage and menisci under weight-bearing conditions. The articular cartilage deformation under different weight-bearing conditions reveals information about abnormal motion patterns, which can be an early indicator for arthritis. This can help to detect the medical condition at an early stage.

    To allow for a scan in standing or squatting position, we opted for a C-arm CT device that can be almost arbitrarily positioned in space. The standard application area for C-arm CT is in the interventional suite, where it usually acquires images using a vertical trajectory around the patient. For the recording of the knees in this project, a horizontal trajectory has been developed.

    Scanning in standing or squatting position makes an analysis of the knee joint under weight-bearing conditions possible. However, it will also lead to involuntary motion of the knees during the scan. The motion will result in artifacts in the reconstruction that reduce the diagnostic image quality. Therefore, the goal of this project is to estimate the patient motion during the scan to reduce these artifacts. One approach is to compute the motion field of the knee using surface cameras and use the result for motion correction. Another possible approach is the design and evaluation of a biomechanical model of the knee using inertial sensors to compensate for movement.

    After the correction of the motion artifacts, the reconstructed volume is used for the segmentation and quantitative analysis of the knee joint tissue. This will give information about the risk or the progression of an arthrosis disease.

     

2010

  • Automatische Analyse von Lautbildungsstörungen bei Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten (LKG)

    (Third Party Funds Single)

    Term: April 1, 2010 - March 31, 2013
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Zur Bewertung von Sprechstörungen von Patienten mit Lippen-Kiefer-Gaumenspalten fehlen bisher objektive, validierte und einfache Verfahren. Im klinischen Alltag werden Lautbildungsstörungen bisher üblicherweise durch eine subjektive, auditive Bewertung erfasst. Diese ist für die klinische und v.a. wissenschaftliche Nutzung nur bedingt geeignet. Die automatische Sprachanalyse, wie sie für Spracherkennungssysteme genutzt wird, hat sich bereits bei Stimmstörungen als objektive Methode der globalen Bewertung erwiesen, nämlich zur Quantifizierung der Verständlichkeit. Dies ließ sich in Vorarbeiten auch auf Sprachaufnahmen von Kindern mit Lippen-Kiefer-Gaumenspalten übertragen. In dem vorliegenden Projekt wird ein Verfahren zur automatischen Unterscheidung und Quantifizierung verschiedener typischer Lautbildungsstörung wie Hypernasalität, Verlagerung der Artikulation und Veränderung der Artikulationsspannung bei Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten entwickelt und validiert. Dies stellt die Basis für die Ermittlung ihres Einflusses auf die Verständlichkeit sowie zur Erfassung der Ergebnisqualität verschiedener therapeutischer Konzepte dar.

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2021

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2020

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2019

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2018

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2017

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2016

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2015

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2014

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2010

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2009

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2005

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Theses

Student Title Type Status
Mahmoud G. A. Sanad Spoken Language Identification for Hearing Aids MA thesis running
Deepak Bhatia Deep Learning-Based Breast Density Categorization in Asian Women MA thesis running
Prakhar Bharadwaj Improvements in SSL image-text learnings on CXR images MA thesis running
Fatma Mami Understanding Odor Descriptors through Advanced NLP Models and Semantic Scores MA thesis running
Md Hasan Generation of Clinical Text Reports from Chest X-Ray Images Project running
Dinuo wei Latent Diffusion Model for CT Synthesis Project running
Hongrun Dong A Comparative Analysis of Loss Functions in Deep Learning-Based Inverse Problems Project running
Attention Artifact! Misalignment and artifact detection using deep learning and augmentation MA thesis running
Asheer Ali Synthetic data creation of defect images for CNN training using GAN MA thesis running
Max Wagner End-to-end detection and 3D localization of implants from multi-view images for surgical CBCT metal artifact avoidance MA thesis running
Jie Yi Tan Pancreatic Duct Detection MA thesis running
Julian Oelhaf Advanced Machine Learning Techniques for Data-Driven Monitoring of Coil Winding Processes in Electric Motor Manufacturing MA thesis running
Graph Neural Networks in Pathological Speech MA thesis open
Nilam Rajak Intensity Grading Using 3D Feature Classification with fMRI Images using Deep-Learning Approach MA thesis running
Karlo Gabriel Fonseca Yakovenko Quantum Machine Learning Techniques in Medical Image Classification: Simulation and Hardware MA thesis running
Leyi Tang Data Encoding, Parameterization and Generalization of Quantum Machine Learning for Medical Imaging MA thesis running
Sri Harsha Vadlamudi Geometry-Aware Key-Point / Object Detection and Pose-Estimation MA thesis finished
Marta López-Brea García A Deep Learning-Based Approach to Analyze Speech of Children with Cleft Lip and Palate MA thesis running
Parteek Parteek Object Detection of more than 100,000 Industrial Parts MA thesis running
Fateme Atayi Deep Learning-based Balloon Marker Detection from Angiography Data MA thesis running
Anna-Sophie Stephan Fitting a 2D to 3D Transformation with Neural Fields for Vessel Unfolding MA thesis running
Anuj Mehta Pretraining Transformers For Predictive Maintenance In Manufacturing MA thesis running
Murali Hemanna Reinforcement learning to learn mean average precision learning MA thesis running
Paul Hannes Zech Deep Learning based Vascular Contouring in Photon-Counting Computed Tomography MA thesis running
Anne Edle von Querfurth Development of an Oriented Bone Detection Algorithm on X-Ray Images MA thesis running
Anne Edle von Querfurth Geometric Domain Adaptation for CBCT Segmentation Project finished
Benjamin El-Zein Realistic Simulation of Collimated X-Ray images for Collimator Edge Segmentation using Deep Learning Project finished
Mingcheng Fan Unsupervised Domain Adaptation Using Contrastive Learning for Multi-modal Cardiac MR Segmentation MA thesis running
Chao Luo Style Transfer on Simulated Mouse Tibia Based on Deep Neural Networks MA thesis running
Chengze Ye Learned Defrise Clack for Specific CBCT Orbits MA thesis running
Junyan Peng Using Reinforcement Learning for Robotic Movement Planning MA thesis running
Yuzhong Zhou Sinogram Analysis Using Attention U-Net: A Methodological Approach to Defect Detection and Localization in Parallel Beam Computed Tomography MA thesis finished
Tianqi Wang Brain Tumour Segmentation Focused on Complex Sub-regions Project finished
Xingjian Kang Deep Reinforcement Learning Based Emergency Department Optimization Project finished
Yihao Hou Fine-tune large language models for radiation oncology MA thesis running
Sven Klaiber Statistical Assessment of Deep Neural Networks in Industrial Applications BA thesis running
Enes Bektürk Optimization and Evaluation of Deformable Image Registration Accuracy for Computed Tomography in Radiation Therapy MA thesis running
Felix Büppelmann Transfer Learning Strategies for Jet Engine Health Monitoring with Full Flight Data MA thesis running
Marc Julian Schwarz “Mainframe Meets AI – Improving Legacy Code Generation Through Fine-tuning of Large Language Models” BA thesis running
Ebru Navruz Dilemma Zone Prediction with Floating Car Data using Machine Learning Approaches MA thesis running
Namratha Narayan Multipath detection in GNSS signals measured in a position sensor using a pattern recognition approach with neural networks MA thesis finished
Investigating the Possibilities of CT Reconstruction using Fourier Neural Operator Project running
Uttam Asodariya Large Language Model for Generation of Structured Medical Report from X-ray Transcriptions MA thesis running
Jakob Spahn Style Transfer of High-resolution Photos to Artworks MA thesis running
Vatsal Harshadhai Bambhania Investigating the benefits of combining CNNs and transformer architecture for rail domain perception task MA thesis running
Regine Büter Eye Tracking and Pupillometry for Cognitive Load Estimation in Tele-Robotic Surgery MA thesis finished
Elisabeth Gabler Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning Project finished
Deepak Parappagoudar Natural Language Text Generation for Symbolic Descriptions Using Language Models MA thesis finished
Philip Drießlein AI for IT Operations (AIOps): Leveraging Large Language Models as Support for Process Management in Mainframes MA thesis finished
Naveed Unjum AI-based generation of 2D vehicle geometries through Natural Language MA thesis running
Uttam Chandubhai Asodariya Word Embeddings Applied to Alzheimer’s Disease Project finished
Gracia Apfelthaler Development of a deep learning approach to detect faulty axial bearing components after assembly using acoustic signals MA thesis finished
Simon Wolfrum Photovoltaic Plant Inspection: Identifying Modules and their Defects in Electroluminesence Imagery MA thesis finished
Wenke Karbole Deep learning-based detection of early biomarker in age-related macular degeneration in volume-merged high resolution optical coherence tomography MA thesis finished
Mark Alexander Vollmar Exploring Stylistic Invariance in Self-Supervised Pretraining for Feature Extraction MA thesis finished
Jianchang Su Service-Oriented Preprocessing for Cost-Effective and Efficient Deep Learning Training MA thesis finished
Stefan Mushevski Automated Wood Identification Using Micro-Computed Tomography on a Cellular Level: A Study with Maple and Pine Wood Samples MA thesis finished
Jingyi Yao End-to-End SMPL Parameter Estimation and Template-to-Scan Registration Tailored Towards Automatic X-ray to CT Initialization MA thesis finished
Marziyeh Mohammadi Contrastive Learning for Glacier Segmentation Project running
Vrinda Gupta Deep Learning-based Detection of Detector Artifacts MA thesis finished
Chengze Ye Learning Reconstruction Filters for CBCT Geometry Project finished
Yuedong Yuan Application of projection-based metrics to the optimisation of arbitrary CT scanning trajectory MA thesis finished
Junyan Peng Tackling Travelling Salesman Problem with Graph Neural Network and Reinforcement Learning Project finished
Tianrui Wu, Mert Özer and Ahmed Khalifa Synthetic Projection Generation with Angle Conditioning Project finished
Fadi Abo Hadba On how to learn and use the Detectability Index efficiently for CT trajectory optimisation BA thesis finished
Lena Augustin Binary Neural Networks for Enhanced Processing in Hearing Aids MA thesis finished
Junyu Shi Gradient-Based Automated Computed Tomography Geometry Correction MA thesis finished
Daniel Mosig Analysis of Federated Learning Approaches for Training Thoracic Abnormality Classification Systems MA thesis finished
Mohammadhossien Sheikhsarraf Edge-AI: Self-sensing backpressure estimation in piezoelectric micropumps using machine learning methods on a limited hardware MA thesis finished
Julian Klink Uncertainty Estimation for Transformer-based Glacier Segmentation Project running
Deep learning brain surface modelling for target volume definition in radiotherapy MA thesis running
Md Muztaba Ahbab A hybrid approach forLeakage Localization in the Water Distribution Network MA thesis running
Martin Geitner Deep learning based information retrieval from technical drawings MA thesis running
Ali Sajdzadeh ML based Classification of States in LPWAN Current Consumption Curves MA thesis finished
Christopher Brückner Evaluation of imperfect segmentation labels and the influence on deep learning models BA thesis finished
Zhengyuan Liu Image-to-Image Translation Using Diffusion Generative Models MA thesis finished
Henrik Willer Recognition of Optical Chemical Structures MA thesis finished
Daniel Augsburger Development of an AI-based ring detection algorithm for CT image quality control MA thesis finished
Adarsh Raghunath Diffusion-based Super Resolution for X-ray Microscopy MA thesis finished
Marcel Dreier Diffusion Models for Generating Offline Handwritten Text Images MA thesis finished
Peter Herbst Unsupervised Contextual Anomaly Detection in Frequency Converter Data MA thesis finished
Elisabeth Gabler Fetal Re-Identification: Deep Learning on Pregnancy Ultrasound Images BA thesis finished
Marco Schnell Adaptive Training of Heat Demand Prediction using Continual Learning MA thesis running
Marcel Reimann Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography Project finished
Vamsi Krishna Annavarapu Optical Character Recognition on Technical Drawings using Deep Learning MA thesis finished
Alma Hanif Offline-to-Online Handwriting Translation using Cyclic Consistency MA thesis finished
Mohamed Albahri Emotion Recognition in Comic Scenes with Multimodal Classifiers MA thesis finished
Kashaf Gulzar Investigation of biases in acoustic embeddings for the detection of Alzheimer’s disease Project finished
Rahul Raj Menon Improving Breast Abnormality Analysis in Mammograms using CycleGAN MA thesis finished
Hassen Ben Tkhayat Deep learning for brain metastases growth prediction MA thesis finished
Majd Helo Deep Learning Reconstruction for Accelerated Water-Fat Magnetic Resonance Imaging MA thesis finished
Nicolas Stellwag Projection Domain Metal Segmentation with Epipolar Consistency using Known Operator Learning BA thesis finished
Mark Antoine Turban Ndjeuha Implementation of an automated optical inspection (AOI) system for the automatic visual inspection of an enclosure assy DC distribution MA thesis finished
Anda Dong Evaluation of a Pixel-wise Regression Model Solving a Segmentation Task and a Deep Learning Model with the Matthew’s Correlation Coefficient as an Early Stopping Criterion BA thesis finished
Christopher Homm Anatomical Landmark Detection for Pancreatic Vessels in Computed Tomography MA thesis finished
Johannes Ohlmann Topology-aware Geometric Deep Learning for Labeling Major Cerebral Arteries MA thesis finished
Volodymyr Marych Similarity Learning for Writer Identification MA thesis finished
Rodolfo Ivo Santos de Andrade Automated lung cancer lesions segmentation in 18F-FDG PET/CT MA thesis finished
Qihan Jiang Object Consistency GAN for Object Detection Pretraining MA thesis finished
Felix Damm Tomographic Projection Selection with Quantum Annealing BA thesis finished
Stefan Ringer Extraction of Treatment Margins from CT Scans for Evaluation of Lung Tumor Cryoablation MA thesis finished
Sulaiman Shamasna Detecting and Transcribing Annotations in Printed Auction Catalogs using Combined Object Detection and Handwritten Text Recognition MA thesis finished
Janina Krüger Image Segmentation and Detection of Imperfections for the Evaluation of Welding Seams using Neural Networks MA thesis finished
Amer Khalouf Classification of Detector Artifacts in Angiographic Imaging using Neural Networks MA thesis finished
Junyu Shi Super-short Scans in Bone XRM Acquisitions Project finished
Lena Augustin Sparse-angle CT Super Resolution using Known Operators Project finished
Jonas Miksch Improving Instance Localization for Object Detection Pretraining MA thesis finished
Shifa e Zainab Sheikh Detecting workflow-states of an MR examination using semantic segmentation of synthetic 3D point-clouds MA thesis finished
Luise Brock T2 Distribution Analysis of Inflamed Bone Marrow Compartments in MR Images with Quantitative T2-mapping MA thesis finished
Christoph Klug Matrix Operations for Applications in Quantum Annealing Project finished
Hassen Ben Development of a comprehensive SPECT phantom dataset using Monte Carlo Simulation Project finished
Kowsar Sheikhi Valashani A Review of Diagnosis Rheumatoid Arthritis, with Evaluating Parameters of Micro-CT Scanner and Laboratory Measurements Project finished
Nastassia Vysotskaya Continuous Non-Invasive Blood Pressure Measurement Using 60GHz-Radar – A Feasibility Study MA thesis finished
Lucas Sedran Optimizing the Preprocessing Pipeline for “virtual Dynamic Contrast Enhancement” in Breast MRI BA thesis finished
Abdelrahman Youssef Detection of positions of K-Wires Tips in X-Ray Images using Deep learning MA thesis finished
Johannes Enk Semi-supervised learning for multi-modal bone segmentation BA thesis finished
Nazmus Sakib Ground Truth based Convolution Kernel Initialization Method for Medical Image Segmentation MA thesis running
Daniel Kraminskiy Evaluation of a Modified U-Net with Dropout and a Multi-Task Model for Glacier Calving Front Segmentation BA thesis finished
Markus Widuch Animal-Independent Signal Enhancement Using Deep Learning BA thesis finished
Christoph Popp reAction: Automatic Speech Recognition in German Automotive Domain MA thesis finished
Amr Hagag Deep Learning for Cancer Patient Survival Prediction Using 2D Portrait Photos Based on StyleGAN Embedding MA thesis finished
Shruthi Rajasekar Risk Classification of Brain Metastases via Deep Learning Radiomics MA thesis finished
Sebastian Probst Simulation of Spike Artifact Obstructed MR Images for Machine Learning Methods Project finished
Benjamin Schuster Automated Scoring of Rey-Osterrieth Complex Figure Test Using Deep Learning BA thesis finished
Aiswarya Uttla Novel View Synthesis for Augmentation of Fine-Grained Image Datasets MA thesis finished
Haobo Song Modelling of the breast during the mammography examination MA thesis finished
Yangkong Wang Metal-conscious Transformer Enhanced CBCT Projection Inpainting MA thesis finished
Simon Wolfrum Detection and Classification of Photovoltaic Modules in Electroluminescence Videos MA thesis finished
Philipp Rost The hippocampus and language: Word to word prediction in terms of the successor representation MA thesis finished
Ibrahim Maniaa Fully Automated Segmentation of Subcutaneous Fat in CT Images MA thesis finished
Elisabeth Heinrich New speech, motor and cognitive exercises for mobile Parkinson’s Disease monitoring with Apkinson BA thesis finished
Hui Yu Unstained White Blood Cells Classification Using Deep Learning MA thesis finished
Represent senor data of district heating network using contrastive learning Project running
Yuzhong Zhou Deep Learning-based XRM Projection Super Resolution Project finished
Adarsh Raghunath Unsupervised Super Resolution in X-ray Microscopy Using a Cycle-Consistent Generative Model Project finished
Bachmaier Magdalena Automatic Rotation of Spinal X-Ray Images MA thesis finished
Alexander Klingebiel Writer Verification/Identification using SuperPoint and SuperGlue BA thesis running
Anindya Banerjee Evaluation of an Attention U-Net for Glacier Segmentation Project finished
Biswarup Chakraborty Evaluation of an Optimized U-Net for Glacier Segmentation Project finished
Varun Bhoj Evaluation of a Bayesian U-Net for Glacier Segmentation Project finished
Akshat Shrivastava Guided Attention Mechanism for Weakly-Supervised Breast Calcification Analysis MA thesis finished
Céline Pöhl Self-supervised learning for pathology classification BA thesis finished
Sheethal Bhat Automatic identification of unremarkable Medical Images MA thesis finished
Tim Raven Human interpretable Writer Retrieval and Verification MA thesis finished
Jovial Silatsa Tchatchum PowerPoint Presentation describer. Machine learning methods to automatically generate business captions from graphics MA thesis finished
Sebastian Müller Detection of localized necking in Hydraulic Bulge Tests using Deep Learning Methods MA thesis finished
Khabbab Zakaria Reinforcement Learning in Optimum Order Execution MA thesis finished
Elina Lesyk Empathetic Deep Learning to the Rescue: Speech Emotion Recognition from Adults to Children MA thesis finished
Luca Manzke Classical Acoustic Markers for Depression in Parkinson’s Disease BA thesis finished
Tri-Thien Nguyen Detection of Arterial Occlusion on MRI Angiography of the Lower Limbs using Deep Learning MA thesis finished
Susmitha Rachamreddy Automated detection and defect recognition of photovoltaic modules in photoluminescence videos MA thesis finished
Silvan Marti Automatic Detection of Microorganisms on Microscopic Images of Fluid Samples using Machine Learning MA thesis finished
Mikhail Kulyabin Synthesizing Art Historical datasets with Pixel-wise Annotations MA thesis finished
Bahraminejad Tahmores Analysis of EEG data with machine learning MA thesis finished
Satyaki Chatterjee Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble MA thesis finished
Jannis Wolf Design and Evaluation of Machine Learning Applications for Space Systems MA thesis finished
Katharina Zinn Learning based methods for 3D hemodynamics estimation in the cerebral vasculature MA thesis finished
Eduard Reger Modeling of Randomized Cerebrovascular Trees for Artifical Data Generation using Blender MA thesis finished
Tobias Frieß Cone-Beam CT X-Ray Image Simulation for the Generation of Training Data BA thesis finished
Klaus Fischer Cardiac Functional Analysis – Automated Strain Analysis of the left Ventricle using Computed Tomography MA thesis running
Eduardo Castaneda Gavina Efficient Methods for Post Myocardial Infarction Ventricular Tachycardia Modeling: from Image Processing to Electrophysiological Simulation MA thesis finished
Marco Martin Härtl Fruit Terminator – Annotation of Lung Fluid Cells via Gamification BA thesis finished
Sun Mengzhou Deep learning method for emotion recognition through the Fusion of body and context features MA thesis finished
Tamara Bäuchle Einfluss der Feldstärke (B0) auf den Intravoxel Incoherent Motion (IVIM) Effekt in der Diffusions-MRT der Wade MA thesis finished
Sebastian Baum Letter Inpainting and Detection of Mathematical Diagrams in Multi-Lingual Manuscripts using a Deep Neural Network approach BA thesis finished
Timo Bohnstedt Determining the Influence of Papyrus Characteristics on Fragments Retrieval with Deep Metric Learning MA thesis finished
Oskar Herrmann Multi-Task Learning for Glacier Segmentation and Calving Front Detection with the nnU-Net Framework MA thesis finished
Hannes Schreiter Virtual Dynamic Contrast Enhanced Image Prediction of Breast MRI using Deep Learning Architectures MA thesis finished
Philipp Sommer The UKER BrainMet Dataset: A brain metastasis dataset from University Hospital Erlangen Project finished
Stefan Fischer Brain Metastasis Synthesis Using Deep Learning in MRI Images MA thesis finished
Ankitha Rama Krishna Reddy GAN-based Synthetic Chest X-ray Generation for Training Lung Disease Classification Systems MA thesis finished
Wolfgang Meier Exploring Style-transfer techniques on Greek vase paintings for enhancing pose-estimation BA thesis finished
Shrihari Muttagi Multi-stage Patch based U-Net for Text Line Segmentation of Historical Documents MA thesis finished
Vojtech Pesek Deep Learning-based Bleed-through Removal in Historical Documents MA thesis finished
Susu Sun Disentangling Visual Attributes for Inherently Interpretable Medical Image Classification MA thesis finished
Vanya Saksena MR automated image quality assessment MA thesis finished
Vishal Sukumar Virtual contrast enhancement of breast MRI using Deep learning MA thesis finished
Julian Klink Temporal Information in Glacier Front Segmentation Using a 3D Conditional Random Field Project finished
Nan Lan Evaluation of Different Loss Functions for Highly Unbalanced Segmentation Project finished
Hatice Demiran Network analysis of soluble factor-mediated autocrine and paracrine circuits in melanoma immunotherapy MA thesis finished
Benjamin Geißler Interpolation of ARAMIS Grids and Analysis of Numerical Stability on Deep Learning Methods Project finished
Sebastian Probst Spike Detection in Gradient Coils of MR Scanners using Artificial Intelligence MA thesis finished
Tri-Thien Nguyen Classification of Cardiomegaly using X-ray of the chest Project finished
Franziska Bauer The German Phonetic Footprint of Parkinsons Disease BA thesis finished
Yinan Shao Similarity and duplicate search in artwork images MA thesis finished
Julian Jendryka Advanced Model Architectures for Interactive Segmentation and Segmentation Enhancement in CT Images MA thesis finished
Muhammad Muqtasid Farooq Learning-based reduction of non-significant changes in subtraction volumes MA thesis finished
Alex Felker Investigating the class-imbalance problem using deep learning techniques on real industry printed circuit board data MA thesis finished
Quirin von Rekowski Digitization of Handwritten Rey Osterrieth Complex Figure Test Score Sheets BA thesis finished
Aishwarya Lakshmi Srinivasan Glioma Growth Prediction Using Reaction-Diffusion Modelling and Machine Learning MA thesis finished
Stephanie Mehltretter Fully Automated Classification of Anatomical Variants of the Coronary Arteries from Cardiac Computed Tomography Angiography MA thesis finished
Philipp Sommer Radiomics, Delta- and Dose-Radiomics in brain metastases MA thesis finished
Chen Ling Writer Identification using Transformer-based Deep Neural Networks MA thesis finished
Sanketa Hedge Deep learning-based respiratory navigation for abdominal MRI MA thesis finished
Srividhya Sathya Narayanan AI-based classification of diffuse liver disease MA thesis finished
Jonas Schauer Cerebral Vessel Tree Estimation from Non-Contrast CT using Deep Learning Methods MA thesis finished
Sarah Seidnitzer Entwicklung von Prozessabläufen für die Forschungszusammenarbeit in datengetriebenen und institutionsübergreifenden Forschungsprojekten Project finished
Linda Vorberg Detection of Pulmonary Embolisms in NCCT Data using Deep Learning Methods MA thesis finished
Ahmed Gomaa Enhancing the robustness and efficiency of multimodal emotion estimation models MA thesis finished
Jessica Pfahlmann Representation Learning with Partial Medical Volumes MA thesis finished
Tobias Doneff Integration of Augmented Reality in SPECT-CT Workflows MA thesis finished
Badhan Das Detection and Prediction of Background Parenchymal Enhancement on MRI Using Neural Network” MA thesis finished
Teena tom Dieck Learnable Feature Space Reductions for Acoustic Representation Vectors BA thesis finished
Jad Kassam Detection of Large Vessel Occlusions using Graph Deep Learning MA thesis finished
Insa Suchantke Erstellung und Evaluierung eines Messprotokolls für die diffusionsgewichtete MRT im Kontext der Screening-basierten Risikostratifikation MA thesis finished
Maximilian Riehl Development and Evaluation of an Transformer-based Deep Learning Model for 12-lead ECG Classification BA thesis finished
Arka Nandi Towards integration of prior knowledge using spatial transformers for segmentation MA thesis finished
Lukas Hüttner Autoencoding CEST MRI Spectra BA thesis finished
Paul Oswald Improvement of Patient Specific SPECT and PET Brain Perfusion Phantoms for Assessment of Partial Volume Effect BA thesis finished
Juan David Leon Parada Deriving procedure definitions from examination data by means of machine-learning MA thesis finished
Thomas Gorges Multi-task Learning for Molecular Odor Prediction With Multiple Datasets MA thesis finished
Paula Schäfer CoachLea: An Android Application to evaluate the progress of speaking and hearing abilities of children with Cochlear Implant BA thesis finished
Viktoria Simon Knowledge distillation for landmark segmentation in medical image analytics MA thesis finished
Alexander Schmidt Enhanced Generative Learning Methods for Real-World Super-Resolution Problems in Smartphone Images BA thesis finished
Saad Munir Deep Learning Classification and Optimization of Manufacturing Process Parameters MA thesis finished
Amit Kumar Sharma Prediction of Steam Turbine Blade Vibration Amplitudes using Machine Learning Methods MA thesis finished
Balaka Dutta Terahertz Image Reconstruction for Historical Document Analysis MA thesis finished
Karim Awad Scene Evolution on Polarimetric Radar Data in Automated Driving Scenarios MA thesis finished
Sai Kishore Goshika Detection of In-plane Rotation of Extremities on X-ray Images MA thesis finished
Kai Packhäuser Analysis of Deep Learning Methods for Re-identification on Chest Radiographs MA thesis finished
Efthymios Oikonomou Two-Dimensional-Dwell-Time Analysis of Ion-Channel Kinetics using Deep Learning MA thesis finished
Simon Langer DeepTechnome – Mitigating Bias Related to Image Formation in Deep Learning Based Assessment of CT Images BA thesis finished
Alexander Mattick Multi-task Learning for Historical Document Classification with Transformers BA thesis finished
Maximilian Rohleder 3D Segmentation of metal objects based on Cone-Beam CT Projection Images for Metal Artefact Removal MA thesis finished
Ute Spiske Interpolation of deformation field for brain-shift compensation using Gaussian Process BA thesis finished
Alexander Schmiedl Covert Channel Vulnerabilities of Online Marketplaces – Impact on Antitrust Laws MA thesis running
Svenja Ottawa Restoring lung CT images from photographs for AI ap- plications MA thesis finished
Abinaya Aravindan Automation of flow cytometry diagnostics workflow for leukemia diagnostics by leveraging machine learning MA thesis finished
Nicolas von Roden Prostate Lesion Detection using Multi-Parametric Magnetic Resonance Imaging MA thesis finished
Adarsh Bhandary Panambur Lung Nodule Classification in CT Images using Deep Learning MA thesis finished
Felix Meister Development of a Fast Biomechanical Cardiac Model for the Treatment Planning of Dilated Cardiomyopathy MA thesis finished
Lisa Kratzke Automatic Deep Learning Lung Lesion Characterization with Combined Application of State-of-the-Art Transfer Learning and Image Augmentation Techniques MA thesis finished
Bhupinder Singh Khural Solution to Extend the Field of View of Computed Tomography Using Deep Learning Approaches MA thesis finished
Christoph Baller Incorporating Time Series Information into Glacier Segmentation and Front Detection using U-Nets in Combination with LSTMs and Multi-Task Learning MA thesis finished
Nikolay Iakovlev torchsense – a PyTorch-based Compressed Sensing reconstruction framework for dynamic MRI MA thesis finished
Ehsan Olyaee Automatic segmentation of whole heart MA thesis finished
Anaga Nayak Height Estimation for Patient Tables from Computed Tomography Data MA thesis finished
Saahil Islam Image Segmentation via Transformers MA thesis finished
Tobias Pertlwieser Learning Multi-Catheter Reconstructions for Interstitial Breast Brachytherapy MA thesis finished
Johar Kanti Sarker Abnormality detection on musculoskeletal radiographs MA thesis finished
Sebastian Dörrich Localization and Standard Plane Regression of Vertebral Bodies in Intra-Operative CBCT Volumes MA thesis finished
Celia Martín Vicario Automatic detection of standard planes in surgical FD-CT volumes MA thesis finished
Shamraiz Ashraf Micro CT Denoising Using Low Parameter Models MA thesis finished
Annette Schwarz Real-Time Prospective Respiratory Triggering for Free-Breathing Lung Computed Tomography MA thesis finished
Kiran Divakar Deep-learning-based behaviour prediction of rear-end road users when changing lane as a system design reference for highly automated driving MA thesis finished
Melanie Kienberger Predictive Maintenance for SINAMICs Frequency Converter MA thesis finished
Grusha Bharat Anandpara Optimization of the fat-water separation for muscle imaging at 7 T with application in quantitative Na23/K39 MRI MA thesis finished
Christoph Feldner Incorporating GAN-Translated Tomosynthesis Images for improved automatic Lesion Detection in Mammography Images MA thesis finished
Amir El-Ghoussani Homogenization of Mammograms using a GAN-based Approach to Improve Breast Lesion Diagnosis MA thesis finished
Disha Dinesh Rao Machine Learning-Based Feature Classification and Position Detection of Spherical Markers in CT Volumes MA thesis finished
Vineet Vinay Bhombore Deep Learning-based image correction for Diffusion Weighted Imaging sequences MA thesis finished
Michael Werthmann Bewertung verschiedener Verfahren zur Lungenregistrierung und möglicher Verbesserungen dieser im Bereich der CT Bildgebung MA thesis finished
Richin Sukesh Synthetic X-rays from CT volumes for deep learning MA thesis finished
Julia Beatriz Yip Automatic characterization of nanoparticles using deep learning techniques MA thesis finished
Marian Plivelic Weakly supervised localization of defects in electroluminescence images of solar cells MA thesis finished
Michael Jechow Detection of Label Noise in Solar Cell Datasets BA thesis finished
Wang, Qian Comparison of different text attention techniques for writer identification MA thesis finished
Haris Habibullah Distillation Learning for Speech Enhancement MA thesis finished
Linh Sam Truong Motion Compensation Using Epipolar Consistency Condition in Computed Tomography MA thesis finished
Paul Stöwer The hippocampus and the Successor Representation – An analysis of the properties of the Successor Representation, place- and grid cells. MA thesis finished
Christian Schlieker Development of a framework to simulate learning and task solving inspired by the hippocampus and successor representation MA thesis finished
Jonas Beyer Semi-Supervised Segmentation of Cell Images using Differentiable Rendering. BA thesis finished
Dmitrij Vinokour Detection of Hand Drawn Electrical Circuit Diagrams and their Components using Deep Learning Methods and Conversion into LTspice Format MA thesis finished
Soroosh Tayebi Arasteh Semi-Supervised Beating Whole Heart Segmentation Based on 3D Cine MRI in Congenital Heart Disease Using Deep Learning MA thesis finished
Marc Vornehm Deep Learning based image enhancement for contrast agent minimization in cardiac MRI MA thesis finished
Jia-Wei Wang Transfer Learning for Re-identification on Chest Radiographs Project finished
Maniraman Periyasamy Getting the Most out of U-Net Architecture for Glacier (Front) Segmentation MA thesis finished
Christian Endres Detecting Defects on Transparent Objects using Polarization Cameras BA thesis finished
Andreas Hartmann Deep Learning-Based Limited Data Glacier Segmentation using Bayesian U-Nets and GANs-based Data Augmentation MA thesis finished
Daniel Amsel Diffeomorphic MRI Image Registration using Deep Learning BA thesis finished
Tilman Marquart Content-based Image Retrieval based on compositional elements for art historical images BA thesis finished
Susanne Fernolendt Absorption Image Correction in X-ray Talbot-Lau Interferometry for Reconstruction BA thesis finished
Niklas Bubeck Truncation-correction Method for X-ray Dark-field Computed Tomography BA thesis finished
Zarei, Shahrooz End-to-End Gaze Estimation Network for Driver Monitoring MA thesis finished
Laura Pfaff Deep-learning-based MR image denoising considering noise maps as supplementary input MA thesis finished
Merlin Nau Mobile 3D-Shape Estimation in Telemedical Dermatologic Diagnosis and Documentation MA thesis finished
Antonia Popp Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning MA thesis finished
Jinho Kim Deep Learning-based motion correction of free-breathing diffusion-weighted imaging in the abdomen MA thesis finished
Jinwei Sun Deep Learning-based Pitch Estimation and Comb Filter Construction MA thesis finished
Clara Zaus Characterizing ultraound images through breast density related features using traditional and deep learning approaches BA thesis finished
Jishnu Jayaraj Dynamic Technology trend monitoring from unstructured data using Machine learning MA thesis finished
Mohamed Soliman Machine-Learning-Based Status Monitoring of HVDC Converter Stations MA thesis finished
Jessica Diehm Detection and semantic segmentation of human faces in low resolution thermal images MA thesis finished
Veronika Schneider Quality Assurance and Clinical Integration of a Prototype for Intelligent 4DCT Sequence Scanning MA thesis finished
Guanxin Jinag A Robust Intrusive perceptual audio quality assessment based on convolutional neural network MA thesis finished
Xueyi Zeng Synergistic Radiomics and CNN Features for Multiparametric MRI Lesion Classification Project finished
Lukas Folle Dilated deeply supervised networks for hippocampus segmentation in MR Project finished
 Benjamin Geißler Multimodal Breast Cancer Detection using a Fusion of Ultrasound and Mammogram Features BA thesis finished
Nico Kaiser Classification of Breast Density in Mammograms Using Deep Machine Learning MA thesis finished
Jalil Ahmed COPD Classification in CT Images Using a 3D Convolutional Neural Network MA thesis finished
Maryam Roohian Tumor Detection & Classification in Breast Cancer Histology Images using Deep Neural Networks MA thesis finished
Dominik Eckert Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation MA thesis finished
Khural, Bhupinder Singh Solution to extend the Field of View of Computed Tomography using Deep Learning approaches MA thesis finished
Nora Steinich Semi-Supervised Tooth Segmentation in Dental Panoramic Radiographs Using Deep Learning MA thesis finished
Sarah Wallraff Age Estimation on Panoramic Dental X-ray Images Using Deep Learning BA thesis finished
Jalpa Parmar Weakly Supervised Learning for Multi-modal Breast Lesion Classification in Ultrasound and Mammogram Images MA thesis finished
Mingxuan Gu Unsupervised Domain Adaptation using Adversarial Learning for Multi-model Cardiac MR Segmentation MA thesis finished
Fuxin Fan Marker Detection Using Deep Learning for Universal Navigation Interface MA thesis finished
Yannik Tannhäuser Synthetic Image Rendering for Deep Learning License Plate Recognition BA thesis finished
Valentin Bacher Learning projection matrices for marker free motion compensation in weight-bearing CT scans BA thesis finished
Lin Yuan Implementation and Evaluation of Cluster-based Self-supervised Learning Methods MA thesis finished
Dorsaf Jdidi Clustering of HPC jobs using Unsupervised Machine Learning on job performance metric time series data BA thesis finished
Jiayue Zhao Optimization of the Input Resolution for Dermoscopy Image Classification Tasks MA thesis finished
Felix Liebezeit Towards Efficient Incremental Extreme Value Theory Algorithms for Open World Recognition MA thesis finished
Nicolas Münster Deep Learning-based Matching of Chest X-Ray Scans BA thesis finished
Roshni Rahul Kotian Early stage inflammatory musculoskeletal diseases classification with deep learning MA thesis finished
Arthur Effting Start, follow, read, stop: Incorporating new steps into end-to-end full-page handwriting recognition method BA thesis finished
Kunal Raikar AI based Localization of Ischemic Heart diseases using Magnetocardiography signals MA thesis finished
Mahnoor Tanveer Rigid Registration of Bones for Freely Deforming Follow-Up CT Scans MA thesis finished
Pflaum, Leonie Estimation and evaluation of a CT image based on electromagnetic tracking data for adaptive interstitial multi-catheter breast brachytherapy MA thesis finished
Jia-Wei Wang CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction Project finished
Tahreem Rasul Deep Learning based Beamforming for Hearing Aids MA thesis finished
Mathias Zinnen Solar Cell Aging Prediction using Deep Learning Image2Image Translation MA thesis finished
Ann-Kristin Seifer Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach MA thesis finished
Pascal Zobel Deep Learning-based Spectral Noise Reduction for Hearing Aids MA thesis finished
Heiko Nille Multi-Task Learning for Speech Enhancement and Phoneme Recognition MA thesis finished
Lena Augustin Development of a pre-processing/simulation Framework for Multi-Channel Audio Signals BA thesis finished
Weakly-supervised segmentation of defects on solar cells MA thesis finished
Using Deep Learning for segmentation of localized defects on SiC wafers MA thesis finished
Learning From Weak Label Sources In Industrial Sensor Systems MA thesis finished
Diagnosis of Gasoline Direct Injection Systems with Neural Networks MA thesis finished
Semantic Segmentation of the Human Eye for Driver Monitoring BA thesis finished
B. Sc. Moritz Hannemann Multi-task Learning for Module Power Prediction and Failure Classification on EL Images MA thesis finished
Jing Ye Multi-task Learning for Historical Handwritten Document Classification BA thesis finished
Angel Villar-Corrales Pose Based Image Retrieval in Greek Vase Paintings MA thesis finished
Benedikt Mielke Adversarial Modeling of Emotions in Visual Scenes BA thesis finished
Luis Carlos Rivera Monroy Emotion Recognition Guided by Gaze and Context on Images MA thesis finished
Vincent Gemar Machine-learning based localization of latest epicardial activation for cardiac resynchronization therapy guidance BA thesis finished
Sacha Medaer Reinforcement Learning for the Planning of Liver Tumor Thermal Ablation MA thesis finished
Ling Liu Deep Learning for Streak Reduction in Computed Tomography MA thesis finished
Lei Gao Superpixel-Based Background Recovery from Multiple Images Project finished
Jingyi Fa Deep Scatter Estimation Real-time CT Scatter Correction MA thesis finished
Al Sabbagh, Tayseer Deep Learning Reconstruction for 23Na Magnet-Resonance-Imaging of the Skeletal Muscle MA thesis finished
Dana Pfeufer Automated Volume of Interest Reconstruction in dedicated Spiral Breast CT MA thesis finished
Robert Stoll High Resolution Low-Dose-CT using Beam Collimation and Limited Projections BA thesis finished
Fuxin Fan Cephalometric Landmark Detection Using Deep Learning Project finished
Lin Yuan Projection Inpainting Using Partial Convolution for Metal Artifact Reduction Project finished
Lei Gao Truncation Correction in Computed Tomography Using Deep Learning MA thesis finished
Nora Gourmelon End-use Classification using High-Resolution Smart Water Meter Data MA thesis finished
Lucie Meißner Ranking Loss for Writer Identification on Music Scores MA thesis finished
Qian Wang Concentrating on Text for Improved Document Analysis MA thesis finished
Zhenghua Wang End-to-end Deep Learning based Writer Identification MA thesis finished
Generative Adversarial Networks for Speech Vocoding MA thesis finished
Lukas Folle Classification of Rotator Cuff Tears in MRI using Neural Networks MA thesis finished
Jayashree Amalvayal Rangarajan Automatic solar panel recognition, fault detection and localization in thermal images MA thesis finished