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



  • The preliminary meeting slides can be found here: MLMISS22.pdf
  • The preliminary meeting is scheduled for Feb 3, 10:00 (Zoom link is visible on TUMonline in the course description).
  • ُُThe lectures and presentations happen in hybrid form (the meeting info 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/exercises 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.

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:

Schedule of Lectures 

DateSession: TopicSlidesLecturer
Ashkan Khakzar
12/05Medical Image Reconstruction
Shahrooz Faghihroohi
19/05Graph Neural Networks
Anees Kazi
Yousef Yeganeh
09/06Generative Models
Azade Farshad
30/06Invited Talk
Thomas Wendler
07/07Invited Talk

Ghazal Ghazaei

14/07Semi-Supervised Methods
Tariq Bdair
21/07Incremental learning

Indu Joshi

28/07Final Presentation



Explaining Medical Image Classifiers with Visual Question Answering Models

MLMI_SoSe22_VQA Models.pdf

Fabian Scherer, Andrei Mancu, Alaeddine Mellouli, Çağhan Köksal

Structured report generation

MLMI_SoSe22_Structured Report Generation.pdf

Yiheng Xiong, Jingsong Liu. Priyank Upadhya, Melis Gülenay

A comprehensive study of Semi-Supervised Learning in Medical Imaging

Bdair, Tariq MLMI_SoSe22_SemiSupervisedLearning.pdf

Mert Sayar, Anna Banaszak, Cenk Eralp, Umaid BIn Zubair

SceneGenie: Scene Graph to Image via CLIP Embeddings and Diffusion Model-based Generation


Chengzhi Shen, Yu Chi, Jacopo Sitran, Tobias Vitt

Exploring generative models for OCT Image generation

MLMI_SoSe22_OCT Image Generation.pdf

Daria Matiunina, Furkan Çelik, Sebastian Richstein, Murilo Bellatini

GeNoMe: Generating Anomalies in Medical Imaging


Andrea Matécsa, Jakob Ropers, Yixuan Hu, Ata Jadid Ahari

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