Index
Advancing Lung Imaging Assessment in Nuclear Medicine
Master Thesis on Advancing Lung Imaging Assessment in Nuclear Medicine
Molecular Imaging of lung ventilation and perfusion allows functional assessment that is clinically useful for managing pulmonary diseases. This project focuses on developing and evaluating new methods for the advanced visualization and automated quantification of three-dimensional lung imaging in nuclear medicine.
Your profile and skills:
- You have programming proficiency with Python
- Familiarity with medical imaging and image processing is a plus
- You work analytically, in a structured and quality-conscious manner
- You are able to work independently and enjoy a collaborative team environment
- You have excellent communication skills in English
Please send your transcript of records, CV, and a small motivation letter on why you would be interested in the topic to maximilian.reymann@fau.de
Evaluating Large Language Models Using Gameplay (ClemBench)
Radiology Report Generation and Evaluation
This master thesis explores the training and evaluation of a vision-language model for radiology report generation using large-scale medical datasets. We aim to investigate how different clinical settings influence the quality of generated reports, with a focus on enhancing the evaluation of the generated reports.
- Dataset preparation
- Finetune vision-language models
- Comprehensive evaluation
- Experience with PyTorch and training models
- Experience with vision or language models
- (Optional) Experience using SLURM
- (Recommended) Deep Learing / Pattern Recognition Lecture
Application: (Applications that do not follow the application requirements will not be considered)
Please send your CV, transcript of records, and short motivation letter (1 page max) with the subject “Application CXR-Report + your_full_name” to Lukas.Buess@fau.de
Start Date: 01.06.2025 or later
Relevant Literature:
[1] Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning. Advances in neural information processing systems, 36, 34892-34916.
[2] Pellegrini, C., Özsoy, E., Busam, B., Navab, N., & Keicher, M. (2023). Radialog: A large vision-language model for radiology report generation and conversational assistance. arXiv preprint arXiv:2311.18681.
[3] Johnson, A. E., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C. Y., … & Horng, S. (2019). MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data, 6(1), 317.
[4] Ostmeier, S., Xu, J., Chen, Z., Varma, M., Blankemeier, L., Bluethgen, C., … & Delbrouck, J. B. (2024). Green: Generative radiology report evaluation and error notation. arXiv preprint arXiv:2405.03595.
Exploring Species-level Similarity in Bayesian Stimulus Priors of Artificial Intelligent Agents
Deep Learning-based Classification of Body Regions in Intraoperative X-Ray Images
Diffusion Transformer for CT artifacts compensation
Computed Tomography (CT) is one of the most important modality in modern medical imaging, providing invaluable cross-sectional anatomical information crucial for diagnosis, treatment planning, and disease monitoring. Despite its widespread utility, the quality of CT images can be significantly degraded by various artifacts arising from physical limitations, patient-related factors, or system imperfections. These artifacts, manifesting as streaks, blurs, or distortions, can obscure critical diagnostic details, potentially leading to misinterpretations and compromising patient care. While traditional iterative reconstruction and early deep learning methods have offered partial solutions, they often struggle with complex artifact patterns or may introduce new inconsistencies. Recently, diffusion models have emerged as a powerful generative paradigm, demonstrating remarkable success in image synthesis and restoration tasks by progressively denoising an image from a pure noise distribution. Concurrently, Transformer architectures, with their inherent ability to capture long-range dependencies via self-attention mechanisms, have shown promise in various vision tasks. This thesis investigates the potential of Diffusion Transformer, for comprehensive CT artifact compensation. By synergizing the iterative refinement capabilities of diffusion models with the global contextual understanding of Transformers, this work aims to develop a robust framework capable of effectively mitigating a wide range of CT artifacts, thereby enhancing image quality and improving diagnostic reliability. This research explores the design, implementation, and rigorous evaluation of such a model, comparing its performance against existing state-of-the-art techniques.