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(WS 23/24) Seminar Deep Learning in Medical Image Analysis

Current information

Thanks to the great amount of interest and encouraging feedback from the previous three seminar installations, we will offer a fourth round of the Deep Learning in Medical Image Analysis seminar in WS2023/24.

The seminar will take place on Wednesday afternoons, with one briefing session and 6 presentation slots. This seminar is mostly targeted at Bachelor-/Master students of the Computer Science and Scientific Computing genre, who have some previous knowledge of neural networks and are interested in more in-depth methodology and application in the medical imaging domain.

The participant number is limited to 12 people. Interested students can contact Tom Rix for a registration request.

The registration for the seminar is closed, no further applications are considered for this semester.


Seminar Logo


This seminar will discuss current research in the field of machine learning-based biomedical image processing. In contrast to general image analysis applications the medical domain provides special challenges that we want to focus on within the seminar:

  • Data scarcity: It is rather common that research on complex medical applications faces the problem of only small amounts of available data. This is rarely due to intrinsic rareness of certain medical cases, but rather to difficulties related to the use of highly sensitive personal information, which is well-protected by law. Current research hence deals with approaches that get by with little or no annotated data at all.
  • Robustness: Often decisive between life and death, algorithms in the medical domain necessarily need to ensure robustness as a criterion. Outliers have to be discovered automatically and treated separately during processing. In a more general sense, the processing systems should themselves be aware about the uncertainty in their computations and provide the user with related quantitative information.
  • Generalizability: Medical applications are highly subject to variability. This includes, for example, different versions and settings of recording devices as well as different modes of handling by physicians. With the intention of broad applicability beyond a specific setting, solid generalizability of the method is required.

 A detailed list of topics will be released around the briefing date.

General information

There will be a briefing session including a presentation and the distribution of the topics as well as an introduction of grading criteria and other requirements for students. This will also provide an opportunity to ask any questions regarding seminar organization.

We will meet in person in Radioonkologisches Entwicklungszentrum (REZ, INF 223) on the following dates:

Briefing session: Wednesday, 18.10.2023, 2 p.m. - 3.30 p.m. (s.t.), online on zoom
Regular sessions: Wednesdays, 2 p.m. - 3.30 p.m. (s.t.), in person

  • 29.11. 
  • 13.12. 
  • 24.01. 
  • 31.01.
  • 06.03.
  • 20.03.
  • 28.02. (Back-up)

[UPDATE: Due to clash of dates we had to cancel and reschedule the sessions on 17.01. and 7.02., that's why there are now sessions in March]

Please send an e-mail to Maike Rees or Tom Rix (for contact details see below). 

The seminar ideally suits students of Computer Science and Scientific computing. We offer both Bachelor- and Master-level topics (Pro-/Seminar), but the main focus lies on advanced techniques, so prior knowledge on Machine Learning, especially Neural Networks, is a precondition.


Prof. Dr. Lena Maier-Hein
INF 223 (REZ), F.01.086

Maike Rees
INF 223 (REZ), F.02.070

Tom Rix
INF 223 (REZ), F.03.038

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