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. Seong Tae Kim, Ashkan Khakzar, Azade Farshad

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: Online Lecture (Please find access information in Moodle)
Time: Thursdays, 16-18
Course Language: English


  • 16-07-2020: Final presentation session will be held on July 30 (Thu.) at 3:30 pm - 6:00 pm.
  • 14-05-2020: Please find lecture materials in Moodle
  • 15-04-2020: Project proposals are available
  • 31-01-2020: Slides for the preliminary meeting are available
  • 20-01-2020: Preliminary meeting: Thursday, 30.01.2020 (16:00-16:30) in CAMP Seminar Room, 03.13.010.
  • 20-01-2020: 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  
    30.01.2020 Preliminary meeting Ashkan, Azade Slides  
    07.05.2020 Intro to DL Shadi    
    14.05.2020 Neural Networks Roger    
    28.05.2020 CNNs, Interpretability Ashkan    
    04.06.2020 Generative Models Azade    
    18.06.2020 Intermediate presentation All tutors    
    25.06.2020 Graph Neural Networks Anees    
    02.07.2020 Robustness Magda    
    09.07.2020 ML for Medical Image Reconstruction Shahrooz    
    30.07.2020 Final Presentation All tutors Slides 3:30pm - 6:00pm


    ID Project Tutor Group I Group II Material
      Beyond U-Net: Learning Medical Image Segmentation from Few Labels Jan Kukačka     Project Description
      Hyperparameter optimization using meta-heuristic approaches Azade     Project Description
      Dropout in Skip‐Autoencoders for Unsupervised Anomaly
    Detection in Brain MRI
    Christoph     Project Description
      EfficientNet? with Robust Training: Team for MICCAI Skin
    Cancer Analysis Challenge
    Seong Tae     Project Description
      Multi-channel MR Image Reconstruction Using Deep Learning Shahrooz     Project Description
      How Good are 3D Segmentation Models? Roger     Project Description

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

    Edit | Attach | Refresh | Diffs | More | Revision r1.14 - 29 Oct 2020 - 21:56 - AshkanKhakzar

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