Index

Covert Channel Vulnerabilities of Online Marketplaces – Impact on Antitrust Laws

Antitrust laws (also referred to as competition laws) are developed to promote vigorous competition, and has the purpose to protect consumers from predatory business practices. The paramount objectives of antitrust law are to guarantee working mechanism of markets as well as ensure a fair competition. A prominent example of infringement of antitrust law is illegal price fixing. By definition, it is an agreement among competitors that stabilize prices or other competitive terms, therefore violating the principle of price establishing mechanism through free-market forces. A typical attribute of illegal price fixing practice is the provable communication (written or oral) between human market participants.

However, in the era of digitalization and e-commerce, the detection of this illegal practice is facing new challenges, since the price establishing mechanism is partially or fully automated (i.e., automated dynamic pricing) and the market participants are not necessarily human beings. Consequently, new technological opportunities are available to hide illegal pricing politics. One possible scenario/risk is to utilize the so-called covert channel to transfer information that facilitate the illegal price fixing practice.

A communication channel is called covert, if it is not originally designed for the communication purpose [1]. Generally, it can be categorized into two groups, namely resource and time channel. To date, it is known as one of the most challenging phenomena in the cyber security. Several publications have demonstrate the applications that use covert channel to transfer critical information [2][3]. The goal of this thesis is therefore to investigate the vulnerability of online market places with regard to illegal price fixing practices under covert channel attack. Following aspects have to be included in this work:

• Literature review of state-of-the-art with regard to covert channel,
• Simulate a price fixing scenario on an e-commerce market place utilizing covert channel to transfer information,
• Comparison of covert channel and conventional communication channel,
• Derive implications and consequences for antitrust law.

[1] Hans-Georg Eßer, Felix C. Freiling. Kapazitätsmessung eines verdeckten Zeitkanals über HTTP, Univ. Mannheim, Technischer Bericht TR-2005-10, November 2005

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

[3] Davide B. Bartolini, Philipp Miedl, and Lothar Thiele. 2016. On the capacity of thermal covert channels in multicores. In Proceedings of the Eleventh EuroSys ’16. Association for Computing Machinery, New York, NY, USA, Article 24, 1–16.

Restoring lung CT images from photographs for AI ap- plications

Motivation: Interstitial lung diseases (ILD) describe a group of acute or chronic diseases
of the interstitium or the alveoli [1]. The diagnosis of ILD is very challenging since there are
more than 200 dierent diseases with each of them occurring only rarely. The modality of
choice for diagnosing ILD is computed tomography (CT), even though the dierent diseases
cause similar or sometimes even identical imaging signs in the lung. Therefore, the results of
the CT-scan have to be combined with additional information like the history of the patient,
the symptoms and the laboratory values [2]. Approaches to assist doctors by including
machine learning algorithms like a similar patient search (SPS) already exist [3]. The idea
is to develop an app to take a photograph of the CT-scan and process the image in order
to start a SPS. The main focus of this work will be on the processing of the photograph in
order to restore the CT-properties of the original scan.
Methods: Taking photographs of a CT-scan on a screen leads to a loss of the Hounseld
Units and introduces artifacts like moire patterns, light and mirroring artifacts and imbalanced
illumination. To restore the lung CT image from a photograph, a traditional
approach using lters in contrast to a deep learning approach will be investigated. The
new approach subtracts the screen pixel array in order to avoid moire patterns, removes
the other most critical artifacts from the photograph and restores the lung CT window by
converting the pixel values of the photograph back into Hounseld Units. The processed
photograph can then be send to the SPS tool in order to help doctors nd the right diagnosis.
The Master’s thesis covers the following aspects:
1. Identication of the most critical artifacts appearing in photographs
2. Investigation of traditional and deep learning based approaches for artifact reduction
3. Determination of reading room conditions
4. Determination of an adequate framework and test criteria
5. Implementation of an image processing algorithm based on a literature research and
the identied artifacts
6. Evaluation of the proposed method
Supervisors: Dr. Daniel Stromer, Dr. Christian Tietjen, Dr. Christoph Speier,
Dr. med. Johannes Haubold, Prof. Dr.-Ing. habil. Andreas Maier

References
[1] B. Schonhofer and M. Kreuter, \Interstitielle lungenerkrankungen,” in Referenz Inten-
sivmedizin (G. Marx, K. Zacharowski, and S. Kluge, eds.), pp. 287{293, Stuttgart: Georg
Thieme Verlag, 2020.
[2] M. Kreuter, U. Costabel, F. Herth, and D. Kirsten, eds., Seltene Lungenerkrankungen.
Berlin and Heidelberg: Springer, 2015.
[3] Siemens Healthcare GmbH, \Similar patient search: syngo.via: Va20a,” 2021.
1

Automation of flow cytometry diagnostics workflow for leukemia diagnostics by leveraging machine learning

Background: FCM – Flow cytometry is a technique for measuring the physical and chemical properties
of individual cells suspended in a fluid stream. FCM is widely used in immunology, in many clinical and
biomedical laboratories for diagnosis, subclassification and post-treatment monitoring of blood cancers or
leukemias. Generally, a single session of FCM produces multidimensional readouts of 10,000 to 1,000,000
cells with 4 to 12 parameters.
The conventional workflow of diagnostics involves visualization of the FCM dataset in a series of 2-D scatter
plots and evaluate the different characteristics of cell populations by experts. Based on the inspection, the
pathologists identify a sub-population of cells (gating) and quantifies for further analysis/diagnosis.
Motivation: However, the conventional analytic process is performed manually on a sequence of two-
dimensional scatter plots. Repeating this process on multiple data sets is very time consuming and labour-
intensive. This problem leads to different clinical decisions depending upon the individuals who perform it
and causes more challenges.
Approach: Our approach is to automatize these conventional workflows by leveraging machine learning
techniques thereby supporting the pathologists/clinicians in their daily routine or research work. The main
objective of this thesis is to focus on the identification of small amounts of residual atypical cells in patients
with leukemia (minimal residual disease – MRD) in an automated fashion.
The following is an overview of the tasks involved in the development of the project:
1. Data Selection: Finding an unsupervised algorithm to search for “islands” that contain mainly events
from the same sample, but only a few events from different samples.
2. Dimensionality Reduction Algorithms[1]: Implementing other algorithms (umap) and validating the
effect against the existing t-SNE algorithm.
3. Optimization: Performing optimization of SNE based on OptSNE algorithm [2].
4. Performing evaluation and testing
References
[1] Y. Saeys, S. Van Gassen, and B. Lambrecht, “Computational flow cytometry: Helping to make sense of
high-dimensional immunology data,” Nature Reviews Immunology, vol. 16, 06 2016.
[2] A. C. Belkina, C. O. Ciccolella, R. Anno, R. Halpert, J. Spidlen, and J. E. Snyder-
Cappione, “Automated optimized parameters for t-distributed stochastic neighbor embedding
improve visualization and allow analysis of large datasets,” bioRxiv, 2019. [Online]. Available:
https://www.biorxiv.org/content/early/2019/05/17/451690

Prostate Lesion Detection using Multi-Parametric Magnetic Resonance Imaging

Lung Nodule Classification in CT Images using Deep Learning

Development of a Fast Biomechanical Cardiac Model for the Treatment Planning of Dilated Cardiomyopathy

Automatic Deep Learning Lung Lesion Characterization with Combined Application of State-of-the-Art Transfer Learning and Image Augmentation Techniques

Solution to Extend the Field of View of Computed Tomography Using Deep Learning Approaches

Incorporating Time Series Information into Glacier Segmentation and Front Detection using U-Nets in Combination with LSTMs and Multi-Task Learning

This thesis aims at integrating time series information into the static segmentation of glaciers and their
calving fronts in synthetic aperture radar (SAR) image sequences. U-Nets have recently been shown
to provide promising results for glacier (front) segmentation using synthetic aperture radar (SAR)
imagery [1]. However, this approach only incorporates the spatial information in a single image. The
temporal information of complete image sequences, each showing one glacier at different time points,
has not been addressed thus far. To fill this gap two approaches shall be worked on:

• approach 1; using Long Short-Term Memory (LSTM) layers in the U-Net architecture:
  Recurrent Neural Networks like LSTMs are designed such that information from previous
  inputs in a sequence can be stored in a memory and used to ameliorate the prediction for
  the current input. The combination of structured LSTMs and Fully Convolutional Networks
  (FCNs) showed promising results for joint 4D segmentation of longitudinal MRI [2]. In [3], a
  U-Net was successfully combined with a bi-directional convolutional LSTM for aortic image
  sequence segmentation outperforming a simple U-Net in segmentation accuracy. In this thesis,
  the combination of LSTMs and U-Nets will be tested for glacier segmentation and calving
  front detection in SAR image sequences. Moreover, the use of Recurrent layers (RNN), Gated
  Recurrent Units (GRU) and bi-directional LSTMS instead of simple LSTMs shall be investigated
  as well.
• approach 2; Multi-Task Learning (MLT): As the region to be segmented for calving front
  detection is a small part of the image, this task shows a severe class-imbalance. To improve its
  performance, an MLT approach shall be implemented jointly training glacier segmentation and
  calving front detection. Performance enhancement of U-Nets have been observed using stacking
  [4] and shared encoding networks [5, 6]. In this thesis, both MLT techniques shall be tested
  using U-Nets in combination with LSTMs (see point 1).

The resulting models will be compared quantitatively and qualitatively with the state-of-the-art and
shall be implemented in Keras.

[1] Zhang et al. “Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal
TerraSAR-X images: a deep learning approach.” The Cryosphere 13, no. 6 (2019): 1729-1741.

[2] Gao et al. “Fully convolutional structured LSTM networks for joint 4D medical image segmentation.”
In: IEEE 15th International Symposium on Biomedical Imaging, Washington, DC, 2018, IEEE, pp.
1104-1108.

[3] Bai et al. “Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations.”
In Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-L´opez, Gabor Fichtinger
(Eds.): Medical Image Computing and Computer Assisted Intervention – MICCAI, 2018, pp. 586-594.

[4] Sun et al. “Stacked U-Nets with Multi-Output for Road Extraction.” In: CVPR Workshops, Salt Lake
City, 2018, pp. 202-206.

[5] Ke et al. “Learning to segment microscopy images with lazy labels.” In: ECCV Workshop on BioImage
Computing, 2020.

[6] Lee et al. “Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice
Activity Detection.” Applied Sciences 10, no. 9 (2020): p. 3230.

torchsense – a PyTorch-based Compressed Sensing reconstruction framework for dynamic MRI

In this master thesis a novel deep learning-based reconstruction method specifically tailored for cardiac radial cine MRI image sequences is investigated. Despite the many advantages presented by state-of-the-art unrolled networks, their applicability is limited due to integration of the forward operator into the scheme which poses a computational challenge within the scope of dynamic non-Cartesian MRI. The novelty of our algorithm constitutes the decoupling of regularization and data consistency enforcement into two separate steps that can be combined into an end-to-end reconstruction scheme which reduces the usage of the forward operator and, thereby, offers more flexibility. In contrast to unrolled networks, the regularization step will be achieved by a lightweight denoising CNN, in some cases leading to a closed-form solution of the data-consistency step.

Utilizing the flexibility (e.g., variable network length at test time), we will seek to increase the undersampling ratio of the k-space, thereby, allowing a higher temporal resolution using an existing acquisition scheme.