Seminar · Earlier teaching at TU Munich (CAMP)
Deep Learning for Medical Applications (DLMA)
A master seminar at TU Munich’s Chair for Computer Aided Medical Procedures (CAMP) on recent research in deep learning for medical applications, together with selected papers from other communities.
About the course
Offered at the Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, under Prof. Nassir Navab, with Shadi Albarqouni as tutor and course organiser.
Deep learning is growing tremendously in computer vision and medical imaging. Highly impactful journals in the medical imaging community, such as IEEE Transactions on Medical Imaging, have published special issues on deep learning. The seminar proposes a list of recent scientific articles on the main current research topics in deep learning for medical applications, together with interesting papers from other communities, for example on supervised, self-, semi- and weakly supervised learning, interpretability, domain adaptation, uncertainty, meta-learning, graph convolutions and spatio-temporal learning.
Learning objectives
- Read, understand and critically assess recent research papers in deep learning for medical applications.
- Present a paper to an audience of peers and lead the discussion.
- Explain the paper in your own words in a blog post for a broader audience.
Target audience
Teaching format
Evaluation
- Presentation of the assigned paper (20 minutes plus 10 minutes of questions).
- Blog post of 1,000–2,000 words (excluding references), written in your own words and submitted two weeks after the presentation.
- Active participation in all seminar sessions.
