Index

Investigating the Influence of Using Different for Detecting Parkinson’s Disease

Integrating Transformer Networks with Multi-Modal Learning for Document Layout Analysis

A Cascaded Encoder–Decoder Network for CT Image Restoration

Evaluate LORA finetuning for detection guided segmentation in CT images

Improving localization of VL models in CXRs

Patient-Specific Quality Assurance of Synthetic CT for MR-Only Radiotherapy

If you are interested in the thesis project, please send your application (CV, letter of motivation, current transcript
of records) with subject ’sCT PSQA – Thesis’ to: bernd-niklas.axer@extern.uk-erlangen.de

Data-Driven Characterization and Modeling of the Radiotherapy Workflow

If you are interested in the project, please send your curriculum vitæ to: rafael.lobao@uk-erlangen.de
Having prior experience with Python, SQL, and statistics is advantageous.

Field of Study: Medical Engineering / Data Science

AI for Inflammatory Skin Pathology: Psoriasis and Eczema Classification in Whole-Slide Images

Exploring foundation models for high-resolution whole-slide image classification, with a focus on interpretable predictions through attention maps that align with medically relevant regions.

Evaluating Time-Frequency Representations for Intelligent Fault Analysis in Power System Protection

This project investigates how different time-frequency representations, including spectrograms, wavelets, scalograms, and Gramian Angular Fields, affect deep learning performance in fault analysis tasks. Using high-resolution current and voltage signals, it benchmarks various representation strategies across multiple neural network architectures. The goal is to determine which transformations capture fault dynamics most effectively and enable robust model generalization. The findings will guide the design of future machine learning based protection systems for resilient and data-driven power grids.

Multi-Task Learning for Integrated Fault Analysis in Power System Protection

Modern electric power systems require rapid and reliable fault analysis to ensure grid stability amid increasing renewable integration. This project explores multi-task learning as a unified framework for simultaneously detecting, classifying, and localizing faults in transmission networks. By sharing representations across tasks, the model aims to reduce redundancy and enhance generalization compared to traditional single-task approaches. The results will contribute to the development of scalable, data-driven protection schemes for future intelligent power grids.