Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghih Roohi, Ashkan Khakzar, Azade Farshad, Anees Kazi
- 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.
- The preliminary meeting slides can be found here: MLMISs21.pdf
- The preliminary meeting is scheduled for Feb 8, 15:00 (Zoom link is visible on TUMonline in the course description).
- Due to the current pandemic, the seminar happens virtually via Zoom (the meeting link will be shared with participants via email).
- The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture/excercises block and a project.
- The lectures will include DL topics relevant to medical imaging applications. Each lecture will be followed by a practical hands-on exercise (e.g. the implementation in Python).
- The topics of the projects 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.
In this Master Praktikum (Hauptseminar), students select one scientific article from the list provided by course organizers. The students read the paper, and must accomplish the following:
- Presentation: 50% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
- Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
- The guideline for mid-presentation can be found here: MidPresentation-Guideline.pdf
- The guideline for the final presentation can be found here: Guideline and agenda for Final presentation (SS2021 MLMI).pdf
- Project Progress: 50% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository.
|08.02.2021 (15:00-16)||Preliminary Meeting||Slides|
|20.04.2021||Announcing the list of projects|
|27.04.2021||Assigning the projects to students|
ML for Medical Imaging (Image reconstruction case study)
|06.05.2021 (16:00-18)||Segmentation and Localization for Medical Imaging||Roger Soberanis|
|13.05.2021||No class (holiday)|
|20.05.2021 (16:00-18)||CNNs, Interpretability||Ashkan Khakzar|
|27.05.2021 (16:00-18)||Generative models||Azade Farshad|
|03.06.2021||No class (regional holiday)|
|17.06.2021 (16:00-18)||Graph neural networks||Anees Kazi|
|24.06.2021 (16:00-18)||Transformers||Yousef Yeganeh|
|01.07.2021 (16:00-18)||Robustness||Magda Paschali|
|08.07.2021||Anomaly Detection & Introduction to convnets.org||Christoph Baur|
AutoML for End-to-End Clustering
Yadunandan Vivekanand Kini
HydraGCN: Multi-modal data analysis framework for medical applications (focusing on Graph Convolutional Networks)
Anees Kazi, Ahmad Ahmadi, Gerome Vivar, Hendrik Burwinkel
Ignacio de los Rios
Multi-Modal and Multi-Task COVID-19 Prediction
|Roger||MLMI_SoSe21_MultiModal MultiTask Prediction.pdf|
Carl Fabian Winkler
Self-Supervised Learning in Vision Transformers
|Azade, Yousef||MLMI_SoSe21_Transformers in OCT.pdf|
Ferran Noguera Vall
Vascular Lesion Detection Using Weakly/Semi-supervised Learning
Paul Konrad Engstler