Bachelor and master thesis will be offered from various research areas. Specific topics will be defined by the chair or in collaboration with the student.
To write a thesis at the chair of Explainable Machine Learning, the following qualifications need to be fulfilled:
- successful exam in a module with lecture and tutorial in Deep Learning or Mathematics for Machine Learning (MSc Thesis), or Machine Learning or Introduction to AI (BSc Thesis)
- successful participation at one of the chair offered seminars or projects
Please refer to VC [Link] for further details.
- "Unveiling CNN Layer Contributions: Application of Feature Visualization in Medical Image Classification Tasks" - Jonida Mukaj supervised by Ines Rieger
- "Generative Data Augmentation in the Embedding Space of Vision Foundation Models to Address Long-Tailed Learning and Privacy Constraints" - David Elias Tafler supervised by Francesco Di Salvo
- „Human Activity Recognition via Deep Learning based on active exoskeleton data”(2.1 MB) - Christoph Zink, supervised by Prof. Dr. Christian Ledig
- "Reproduction of Selected State-of-the-Art Methods for Anomaly Detection in Time Series Using Generative Adversarial Networks" - Anastasia Sinitsyna, supervised by Ines Rieger
- “Benchmarking selected State-of-the-Art Baseline Neural Networks for 2D Biomedical Image Classification, Inspired by the MedMNIST v2 Framework”(8.4 MB) - Julius Brockmann, supervised by Sebastian Dörrich
- “Addressing Continual Learning and Data Privacy Challenges with an explainable kNN-based Image Classifier”(18.9 MB) - Tobias Archut supervised by Sebastian Dörrich
- “Development of a dataset and AI-based proof-of-concept algorithm for the classification of digitized whole slide images of gastric tissue”(5.1 MB)- Tom Hempel supervised by Prof. Dr. Christian Ledig
- "CNN-based Classification of I-123 ioflupane dopamine transporter SPECT brain images to support the diagnosis of Parkinson’s disease with Decision Confidence Estimation"(12.5 MB)- Aleksej Kucerenko supervised by Prof. Dr. Christian Ledig
- " Development of an AI-based algorithm for the classification of gastric tissue for computational pathology"(7.7 MB)- Philipp Andreas Höfling supervised by Prof. Dr. Christian Ledig
- "Component ldentification for Geometrie Measurements in the Vehicle Development Process Using Machine Learning"- Tobias Koch supervised by Prof. Dr. Christian Ledig