Computer Assisted Medical Interventions

(WS 21/22) Seminar Deep Learning in Medical Image Analysis


(01.09.2021) The next installment of the seminar is taking place in WS 2021/22.

- Registration is now OPEN. Please send an e-mail to Thuy Nuong Tran, the contact is listed below. You will receive a confirmation of registration mail.

- The seminar is limited to 12 participants and the spots are assigned on a FIRST-COME, FIRST-SERVE basis. When the limit is reached, you are put on a waiting list.

- Please attend the briefing session to confirm your participation. Anyone who is absent without previous notification will yield their spot to the next person on the waiting list.

- The briefing session is expected to commence on the 25.10.2021. On that day, tasks and seminar guidelines are presented and assignments are finalized. 


We are looking forward to many interesting talks and conversations.


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 another 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 subjected 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 regularly every two weeks on Mondays from 14:00 till 15:30, expectedly starting from October 25th until February 14th. There will be no meeting during Christmas holidays (last meeting before: 06.12. & first meeting afterwards: 24.1.). Meetings will be fully virtual. 

Either register directly during the briefing or send an e-mail to Nuong Tran (for contact details see below). Registrations are open now.

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


Patrick Godau
INF 223 (REZ), F.01.088

Thuy Nuong Tran
INF 223 (REZ), F.02.022

to top
powered by webEdition CMS