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. Shadi Albarqouni, Anees Kazi, Roger Soberanis, Anjany Sekuboyina, Ashkan Khakzar, Matthias Grimm, Walter Simson

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Thursdays, 8:15-10
Course Language: English


  • 17-07-2019: Our final presentation will be on Thursday at 12 pm in our seminar room. Please see below the guidelines.
  • 18-04-2019: Projects are assigned! Please contact your tutor this week to define a regular weekly meeting.
  • 14-04-2019: Projects are announced! Please, send us your preferences (at least three preferences) by email to shadi.albarqouni@tum.de by Thursday, April 18th. Please make sure the subject of your email is "MLMI_Projects_Preferences". Otherwise, it won't be considered.
  • 05-02-2019: Registration, through TUM Matching Platform, should be done between 07.02.19 to 13.02.19
  • 11-01-2019: An introductory meeting: Monday, 04.02.2019 (12:30-13:15) in CAMP Seminar Room, 03.13.010.
  • 08-01-2019: 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.


  • Interested students should attend the introductory meeting to enlist in the course.
  • 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).
  • A maximum number of participants: 20.


  • 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
  • Basic knowledge of C++ is advantageous (in case you decide to use c++ for your final project): knowledge of main concepts of object-oriented programming, basic software engineering, image processing libraries, and linear algebra libraries.


Date Topic By Slides Homework
04.02.2019 Preliminary Meeting Shadi Slides  
Online Project Assignment Shadi    
25.04.2019 No Class      
02.05.2019 Intro. to Deep Learning Shadi    
09.05.2019 Neural Networks (NN) Roger Slides Hands-on
16.05.2019 Convolutional Neural Networks (CNN) Ashkan Slides Hands-on
23.05.2019 Convolutional Auto-Encoders (CAE) Matthias Grimm Slides  
06.06.2019 Generative Adversarial Networks (GAN) Azade Slides Hands-on
13.06.2019 Intermediate Presentation all tutors Guidelines  
04.07.2019 Graph Convolutional Networks Anees Slides Evaluation sheets
11.07.2019 Recurrent Neural Networks (RNN/LSTM) Anjany    
18.07.2019 No Class      
25.07.2019 Final Presentation all tutors CAMP Seminar Room, 03.13.010
Time: Thursdays, 12-14


  • 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.


ID Project Tutor Group I Group II Material
1 DRIVE: Digital Retinal Images for Vessel Extraction Anees Daniel, Anastasia Yupeng, Markus Project Description
2 Chest X-ray Classification and Localization Ashkan Ayesha, Mirac Sridivya, Anindya Project Description
3 X-Ray Landmark Detection Matthias Stefan, Rameshwara Felix, Anum Project Description
4 Attention and Focal Loss in Single-Organ Segmentation Roger Louise, Kim Linh Luis Francisco, Sircharan Project Description
5 SoS? Inversion in Medical Ultrasound Walter Maria, Tom Nuno Fernando, Mansoor Project Description

Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni, Anees Kazi, Roger Soberanis, Anjany Sekuboyina, Ashkan Khakzar, Matthias Grimm, Walter Simson
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN2106)
Term: 2019SoSe

Edit | Attach | Refresh | Diffs | More | Revision r1.35 - 17 Jul 2019 - 18:29 - ShadiAlbarqouni

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