Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Practical Course:
Machine Learning in Medical Imaging

Prof. Nassir Navab, Dr. Shahrooz Faghih Roohi, Dr. Seong Tae Kim, Ashkan Khakzar, Azade Farshad

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: TBA (Due to the pandemic situation we will have lectures online.)
Time: Thursdays, 16-18
Course Language: English


  • 23-10-2020: Course schedule is announced in Moodle
  • 16-07-2020: Please submit your application here. You also have to register via matching system. Using our application form is not mandatory, but it will help you get a higher ranking in the matching system from our side.
  • 30-06-2020: Preliminary meeting: Thursday, 16.07.2020 (16:00-16:30) in virtual meeting room (zoom). Zoom access information is announced in Moodle
  • 30-06-2020: Contact information-If you have any question for this seminar, please feel free to contact Dr. Seong Tae Kim (seongtae.kim@tum.de)
  • 30-06-2020: Website is up!

About the Course

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 topics in image processing and analysis, unsupervised methods, and supervised methods. 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. The programming language for the projects is to be decided in agreement with your supervisor.


  • Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn't been reached (please pay attention to the Deadlines).
  • Maximum number of participants: 20.
  • Requirements

    • Good knowledge of machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
    • Strong knowledge of Python programming language
    • Background in image processing


    • 60% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository)
    • 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
    • Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.


    Please find lecture materials in Moodle

    Date Topic By Slides  
    16.07.2020 Preliminary meeting Dr. Shahrooz Faghih Roohi, Dr. Seong Tae Kim, Ashkan Khakzar, Azade Farshad Slide  
    19.11.2020 Invited Talk: Towards Deep Federated Learning in Healthcare Dr. Shadi Albarqouni, Helmholtz AI    
    26.11.2020 ML for Medical Imaging (Image reconstruction case study) Dr. Shahrooz Faghihiroohi    
    03.12.2020 Neural networks Roger    
    10.12.2020 CNNs, Interpretability Ashkan    
    17.12.2020 Intermediate presentation Dr. Shahrooz Faghih Roohi, Dr. Seong Tae Kim, Ashkan Khakzar, Azade Farshad    
    14.01.2021 Generative models Azade    
    21.01.2021 Graph neural networks Anees    
    28.01.2021 Robustness Magda    
    25.02.2021 Final presentation Dr. Shahrooz Faghih Roohi, Dr. Seong Tae Kim, Ashkan Khakzar, Azade Farshad    


    ID Project Tutor Group I Group II Material
      Dissection of Covid-19 Prediction Models Ashkan, Seong Tae Jonas - Icxel - Sabrina - Nikolaus   Project Description
      Interpreting Covid-19 Prediction Models using Information Bottleneck Ashkan, Seong Tae Wejdene -Yucheng - Yang - Yuezhi   Project Description
      AutoML? in Federated Learning Azade, Yousef Richard - Maximilian - Johann   Project Description
      Unsupervised multimodal image registration using generative networks between imbalanced domains Farid Yue - Muhammad - Magdalena - Philipp   Project Description
      Brain signal analysis using graph convolutional networks Anees, Shahrooz Viktoria - Leela Sai - Ahmed - Beiyan   Project Description

    Title: Machine Learning in Medical Imaging
    Professor: Prof. Nassir Navab
    Tutors: Dr. Shahrooz Faghih Roohi; Dr. Seong Tae Kim, Ashkan Khakzar, Azade Farshad,
    Type: Praktikum
    Information: 6 SWS, 10 ECTS Credits (Module IN2106)
    Term: 2020WiSe

    Edit | Attach | Refresh | Diffs | More | Revision r1.14 - 20 Apr 2021 - 13:09 - ShahroozFaghihroohi

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