Lab · Earlier teaching at TU Munich (CAMP)
Machine Learning in Medical Imaging (MLMI)
A practical course at TU Munich’s Chair for Computer Aided Medical Procedures (CAMP): lectures with hands-on exercises on machine learning for medical images, followed by a supervised group project.
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.
The aim of the course is to give students a working knowledge of machine learning techniques for medical imaging. The course has two parts: a block of lectures and exercises, and a project. The lectures cover image processing and analysis, unsupervised and supervised methods and, in later years, deep learning (neural networks, CNNs, generative and recurrent models, robustness), each followed by a hands-on exercise in Python. Project topics are distributed at the beginning of the semester; each is supervised by a different tutor and carried out in pairs.
Learning objectives
- Apply machine learning methods to medical image analysis problems.
- Implement and evaluate models in Python in hands-on exercises.
- Carry out a research-oriented project in pairs, from problem definition to final presentation.
Target audience
Prerequisites
- Good knowledge of machine learning methods and terminology (classification, regression, data sets, cross-validation).
- Strong knowledge of Python.
- A background in image processing.
Evaluation
- 60%: project progress, assessed by the tutor (mainly weekly progress in the project repository).
- 40%: intermediate and final presentations, assessed by all tutors.
Semesters
| Semester | Course | Format | Audience | Lecturers & tutors |
|---|---|---|---|---|
| WS 2019/20 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) |  Nassir Navab Shadi Albarqouni Roger D. Soberanis-MukulAshkan KhakzarWalter SimsonMagda PaschaliAzade Farshad |
| SS 2019 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabShadi Albarqouni Anees KaziRoger D. Soberanis-MukulAnjany SekuboyinaAshkan KhakzarMatthias GrimmWalter Simson |
| WS 2018/19 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabShadi Albarqouni Christoph BaurMagda PaschaliHuseyin CoskunAbhijit Guha-RoyAnees KaziRoger D. Soberanis-MukulAnjany Sekuboyina |
| SS 2018 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabShadi AlbarqouniChristoph BaurMagda PaschaliHuseyin CoskunAbhijit Guha-RoyAnees KaziRoger D. Soberanis-Mukul |
| WS 2017/18 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabShadi AlbarqouniChristoph Baur Mai BuiHuseyin CoskunAbhijit Guha-RoyAnees KaziRoger D. Soberanis-Mukul |
| WS 2016/17 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabDiana MateusTingying PengShadi AlbarqouniSailesh ConjetiChristian Rupprecht Nicola Rieke |
| SS 2016 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabDiana MateusSailesh ConjetiShadi AlbarqouniNicola RiekeMarco EspositoPascal Fallavollita |
| WS 2015/16 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabLoic PeterDiana MateusFausto MilletariSailesh ConjetiShadi AlbarqouniPascal Fallavollita |
| SS 2015 | Machine Learning in Medical Imaging (MLMI) | Lab | Earlier teaching at TU Munich (CAMP) | Nassir NavabLichao WangTingying PengDiana MateusLoic PeterBenjamin Gutierrez BeckerShadi AlbarqouniFausto Milletari |