Index

A Deep Reinforcement Learning Approach for Optimization of Link Adaptation in 5G Networks and Beyond

Segmentation of OCT Biomarkers in Retinal Diseases using Deep Learning methods

This thesis focuses on the segmentation of Optical Coherence Tomography (OCT) images are used to assist in the diagnosis and treatment of several retinal diseases, such as age-related macular degeneration (AMD) and vitreomacular interface disorders (VID). The study uses the U-Net architecture for AMD to perform multiclass segmentation of biomarkers, specifically drusen, scars, and fluids. The performance of the standard U-Net is compared with various advanced U-Net architectures to determine the most effective model. Similarly, for VID, the segmentation task focuses on identifying macular holes, and the results from the U-Net model are compared with those from more sophisticated U-Net variants. Through extensive experimentation and analysis, this research aims to enhance the accuracy and reliability of OCT image segmentation, contributing to better diagnostic tools for these vision-threatening conditions.

Network Modelling of RNA-seq Data for Chronic Lymphocytic Leukemia

Automated Detection and Analysis of Photoreceptors in Retinal Imaging

Abstract:
Photoreceptor analysis is crucial for understanding retinal structure and function. This research focuses on automated detection and analysis of cones in retinal images acquired through confocal and calculated imaging techniques. Initially, Images are analyzed using state-of-the-art segmentation methods to extract detailed information. Then, data from both modalities are integrated to achieve comprehensive identification of all detectable cones. Future work includes exploring the potential to detect rod cells in the retinal images for a more holistic understanding of retinal structure.

Analysis of Different Optimization Strategies for an Adversarial Chest X-ray Anonymization Approach

Chest X-ray Anonymization and Utility Preservation Using Deep Learning-based Techniques

AI-based Anomaly Detection in Process Signals for Condition Monitoring of Industrial Machines

Thesis Description

Machine shops usually rely on Computer Numerical Control (CNC) controlled Machine Tools (MTs). The impact of downtime of a machine is seen as the biggest concern for the operation of these MTs, followed by machine stop issues [1]. In [1] Adu-Amankwa et al. estimate that around 55.000e could be saved per machine per year using Predictive Maintenance (PdM) techniques.

In the modern industry, PdM is used to improve the decision-making process for the maintenance activity and consequently strives to reduce downtime [2]. Contrary to established strategies, maintenance is performed based on the estimated health of the equipment rather than a planned schedule or after failure [3]. As predictive maintenance usually requires a lot of data, Machine Learning (ML) techniques are commonly used in this field [2].

Two of the most common types of ML are supervised and unsupervised learning. Unsupervised approaches don’t need labeled data, while this is a requirement for supervised learning [4]. The data provided by the machines is unlabeled and only a few anomalies are contained in the dataset. Therefore for this thesis, an unsupervised approach is taken. One of the possible applications for unsupervised learning is the field of Anomaly Detection, which will be utilized in this thesis [4].

Anomaly Detection is the process of finding patterns in the presented data, that lie outside of the expectations. It finds its application in fraud detection for credit cards, insurance or healthcare, military surveillance, and fault detection of critical systems [5]. The manufacturing industry faces the challenge that only a small percentage of anomalies can be detected beforehand. A rotating piece of equipment will deteriorate over time which leads to abnormal behavior which should be considered as a caution of the current state of the equipment. As manufacturing sensor data is generally time-based and are collected over longer periods it makes it difficult to use in typical ML techniques. Therefore techniques that can be used with input sequences of variable length are required [6].

Neural Networks using recurrent Autoencoders which try to reconstruct the input data is one of the state of-the-art methods for time series Anomaly Detection [7]. Other methods include the Local Outlier Factor algorithm for the calculation of the distance metric between time series [8] and the utilization of a Generative Adversarial Network (GAN) including LSTMs (Long Short Term Memory) to capture temporal correlation [9]. Another method of anomaly detection is the analysis of the spectrogram generated by Short-Time Fourier Transforms of the time series [10].

Autoencoders learn only the most significant features, due to the compression to a compact hidden representation, are commonly used. Anomalies are usually missing representative features and, therefore Autoencoders fail to reconstruct the input [11]. This approach relies on the fact that the training data does not contain any anomalies, as anomalies being present in the training data can have a negative effect on the hidden representation and result in bad performance for this kind of data [7].

In this thesis, the unlabeled error data of the CNC MTs will be analyzed and different techniques for Anomaly Detection will be tested. Another part of the thesis will to identify the likely component that caused the error.

In summary, the thesis deals with the following topics:

  1. Data analysis
    (a) Pre-Processing
    (b) Manual identification of anomalies
  2. Development of various Deep Learning Models for Anomaly Detection
  3. Evaluation of the Deep Learning Models regarding various metrics including
    (a) Accuracy
    (b) Training performance
    (c) Runtime performance
  4. Suggest component for Anomaly data

References

[1] Kwaku Adu-Amankwa, Ashraf K.A. Attia, Mukund Nilakantan Janardhanan, and Imran Patel. A predictive maintenance cost model for cnc smes in the era of industry 4.0. The International Journal of Advanced Manufacturing Technology, 104(9):3567–3587, Oct 2019.

[2] Tiago Zonta, Cristiano Andr´e da Costa, Rodrigo da Rosa Righi, Miromar Jos´e de Lima, Eduardo Silveira da Trindade, and Guann Pyng Li. Predictive maintenance in the industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150:106889, 2020.

[3] Gian Antonio Susto, Alessandro Beghi, and Cristina De Luca. A predictive maintenance system for epitaxy processes based on filtering and prediction techniques. IEEE Transactions on Semiconductor Manufacturing, 25(4):638–649, 2012.

[4] Ritu Sharma, Kavya Sharma, and Apurva Khanna. Study of supervised learning and unsupervised learning. International Journal for Research in Applied Science and Engineering Technology, 8(6):588–593, 2020.

[5] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv., 41, 07 2009.

[6] Kamat, Pooja and Sugandhi, Rekha. Anomaly detection for predictive maintenance in industry 4.0- a survey. E3S Web Conf., 170:02007, 2020.

[7] Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S. Jensen. Anomaly detection in time series with robust variational quasi-recurrent autoencoders. In 2022 IEEE 38th International Conference on Data Engineering (ICDE), pages 1342–1354, 2022.

[8] Wang Yong, Mao Guiyun, Chen Xu, and Wei Zhengying. Anomaly detection of semiconductor processing data based on dtw-lof algorithm. In 2022 China Semiconductor Technology International Conference (CSTIC), pages 1–3, 2022.

[9] Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Tadgan: Time series anomaly detection using generative adversarial networks. In 2020 IEEE International Conference on Big Data (Big Data), pages 33–43, 2020.

[10] Hongzu Li and Pierre Boulanger. Structural anomalies detection from electrocardiogram (ecg) with spectrogram and handcrafted features. Sensors, 22(7), 2022.

[11] Ane Bl´azquez-Garc´ıa, Angel Conde, Usue Mori, and Jose A. Lozano. A review on outlier/anomaly detection in time series data. ACM Comput. Surv., 54(3), apr 2021.

 

 

Implementing a Pseudo-3D Technique for virtual Dynamic Contrast Enhancement

Lightweight Early Forest Fire Detection from Unmanned Aerial Vehicles based on Spatial-Temporal Correlation

Calving Fronts and How to Segment Them Using Diffusion Networks

Global warming is impacting every part of our planet, and is also responsible for the rise of sea levels
around the world, posing a threat to a majority of the world’s population living in coastal areas. While
there are multiple factors contributing to sea level rise (SLR), such as thermal expansion due to warmer
oceans, it is also in greater part caused by the melting of glaciers and ice regions which stream into
the ocean [1]. It is therefore important for us to understand and monitor glacier ice loss, specifically
for marine- or lake-terminating glaciers. We can do so by looking at calving front movement, where
calving fronts represent the border between an ocean and a glacier. Delineating this exact front position
is fundamental for analysing the health of our glaciers and how global warming is impacting them.
Manually delineating calving fronts is incredibly time intensive, which is why in recent years, researchers
have started automating this process by turning towards deep learning algorithms. Gourmelon et
al. [2] used a U-Net for segmenting SAR images into different regions and then extracted the calving
front in a post-processing step. Wu et al. [3] combined two U-Nets to develop a cross-resolution
segmentation method, which improves the network’s ability to classify the calving front by having
coarse and fine-grained feature maps interact with each other through an attention-based hooking
mechanism.
Diffusion models have made headlines over the past year for their ability to produce fantastically
realistic images [4]. Since the inception of diffusion models, researchers have also started using them
for image segmentation, like in SegDiff [5], which has been further explored in the medical field
with EnsemDiff [6], as well as MedSegDiff and MedSegDiff-V2 [7, 8]. In the field of calving front
delineation however, using diffusion models has not yet been tested, which is what the focus of this
thesis will be.

In detail, the thesis consists of the following parts:
• a literature review of diffusion models being used for image segmentation tasks,
• a review of diffusion models to segment SAR calving front images into different zones,
• using a diffusion model to directly segment calving front positions,
• comparing the created diffusion model against other methods that were evaluated on the CaFFe
dataset [9].

 

References
[1] Hans-Otto P¨ortner, Debra C Roberts, Val´erie Masson-Delmotte, Panmao Zhai, Melinda Tignor, Elvira
Poloczanska, and NM Weyer. The ocean and cryosphere in a changing climate. IPCC special report on the
ocean and cryosphere in a changing climate, 1155, 2019.
[2] Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein. Calving
fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front
extraction from synthetic aperture radar imagery. Earth System Science Data, 14(9):4287–4313, 2022.
[3] Fei Wu, Nora Gourmelon, Thorsten Seehaus, Jianlin Zhang, Matthias Braun, Andreas Maier, and Vincent
Christlein. Amd-hooknet for glacier front segmentation. IEEE Transactions on Geoscience and Remote
Sensing, 61:1–12, 2023.
[4] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural
information processing systems, 33:6840–6851, 2020.
[5] Tomer Amit, Tal Shaharbany, Eliya Nachmani, and Lior Wolf. Segdiff: Image segmentation with diffusion
probabilistic models. arXiv preprint arXiv:2112.00390, 2021.
[6] Julia Wolleb, Robin Sandk¨uhler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin. Diffusion
models for implicit image segmentation ensembles. In International Conference on Medical Imaging with
Deep Learning, pages 1336–1348. PMLR, 2022.
[7] Junde Wu, Huihui Fang, Yu Zhang, Yehui Yang, and Yanwu Xu. Medsegdiff: Medical image segmentation
with diffusion probabilistic model. arXiv preprint arXiv:2211.00611, 2022.
[8] Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, and Yanwu Xu. Medsegdiff-v2: Diffusion based medical
image segmentation with transformer. arXiv preprint arXiv:2301.11798, 2023.
[9] Nora Gourmelon, Thorsten Seehaus, Julian Klink, Matthias Braun, Andreas Maier, and Vincent Christlein.
Caffe-a benchmark dataset for glacier calving front extraction from synthetic aperture radar imagery. In
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pages 896–898. IEEE,
2023.
[10] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming
Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in PyTorch. In NIPS
Autodiff Workshop, 2017.