Machine Learning in Medical Imaging
Overview
- The MLMI practicum has the following objectives:
- Apply ML in a medical-imaging project – translate a real medical problem into a workable ML pipeline and iterate on it over the semester.
- Deliver supervised, topic-driven work – start from an assigned project topic and work under a dedicated supervisor/tutor.
- Collaborate effectively in a team of four – plan tasks, divide responsibilities, integrate work, and resolve issues as a 4-student group.
- Communicate results professionally – prepare and deliver intermediate and final project presentations.
- Maintain continuous, trackable progress – use a structured workflow with regular updates
- The course is mainly defined by a project.
The project topics will be distributed at the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realized by couples.
The preliminary meeting for the winter semester 2026 is scheduled on Tuesday, 07.07.2026, with the following agenda:
Machine Learning in Medical Imaging (MLMI): 11:00 hrs. - 11:30 hrs.
Deep Learning for Medical Applications (DLMA): 11:30 hrs. - 12:00 hrs.
The sessions will be conducted by the following Teams link:
https://teams.live.com/meet/9346917223863?p=qnzuq86KUhi8X0pe00Please find the latest information on the course Wiki:
https://collab.dvb.bayern/spaces/TUMmlmi/pages/2877458065/MLMI+Winter+2026-2027
Registration
- Registration must be done through TUM Matching Platform (please pay attention to the Deadlines)
- In order to increase your priority, please also apply via our own Registration system.
- The maximum number of participants: 20.