Instructors: Prof. Dr. Nassir NavabDr. Shahrooz Faghih Roohi,  Ashkan Khakzar, Azade Farshad, Anees Kazi



  • 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.

Course Structure

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:


DateSession: TopicSlidesLecturer
08.02.2021 (15:00-16)Preliminary MeetingSlides
20.04.2021Announcing the list of projects

27.04.2021Assigning the projects to students

29.04.2021 (16:00-18)

ML for Medical Imaging (Image reconstruction case study)

Shahrooz Faghihroohi
06.05.2021 (16:00-18)Segmentation and Localization for Medical Imaging
Roger Soberanis
13.05.2021No class (holiday)

20.05.2021 (16:00-18)CNNs, Interpretability
Ashkan Khakzar
27.05.2021 (16:00-18)Generative models
Azade Farshad
03.06.2021No class (regional holiday)

10.06.2021 Intermediate presentation

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
08.07.2021Anomaly Detection & Introduction to 
Christoph Baur
15.07.2021Final presentation



AutoML for End-to-End Clustering

Azade, Yousef

Roland Würsching

Yuqi Fang

Yadunandan Vivekanand Kini

Wang, Xi

HydraGCN: Multi-modal data analysis framework for medical applications (focusing on Graph Convolutional Networks)

Anees Kazi, Ahmad Ahmadi, Gerome Vivar, Hendrik Burwinkel


Mohammed Kamran

Ignacio de los Rios

Ekin Karabulut

Alexander Schwarz

Multi-Modal and Multi-Task COVID-19 Prediction

RogerMLMI_SoSe21_MultiModal MultiTask Prediction.pdf

Sraddha Das

Johannes Hingerl

Carl Fabian Winkler

Faruk Cankaya

Self-Supervised Learning in Vision Transformers

Azade, YousefMLMI_SoSe21_Transformers in OCT.pdf

Xingzhuo Yan

Felix Hartwig

Ferran Noguera Vall

Benedikt Rank

Vascular Lesion Detection Using Weakly/Semi-supervised Learning

Ashkan, ShahroozMLMI_Project_Summer2021_AshkanShahrooz.pdf

Tianhao Lin

Zhixiong Zhuang

Patrick Stecher

Paul Konrad Engstler

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