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

THIS WEBPAGE IS DEPRECATED - please visit our new website

Practical Course:
Machine Learning in Medical Imaging

Nassir Navab, Diana Mateus, Olivier Pauly, Loic Peter, Sebastian Poelsterl

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Wednesdays 11:00 - 12:30
Course Language: English


  • The Lecture of the 31st of October will take place from 12:30 to 14:00
  • Information meeting will take place in the student-lab 03.13.038 on Wednesday 17th of October Important organizational meeting will be provided.

About the Course

In this course students will learn through practice the application of different machine learning methods to problems involving medical images. The Master-Praktikum will consist in:

  • (1) a few introductory lectures on machine learning and its application in different problems involving medical imaging,
  • (2) a number of exercises to apply different learning approaches to toy example data,
  • (3) a machine learning project with a real medical application to be solved in groups (pairs).

Regular meetings will be held individually with each group to follow up the progress of the project along the semester

Presentation and Report


  • Registration through TUMOnline.
  • Maximum number of participants: 20.


  • Basic knowledge in MATLAB and C/C++ programming is an advantage.


Date Topic By Slides Homework
October 17, 2012 Information Meeting Diana Mateus Info meeting slides
Introduction to Probability
October 24, 2012 Image Representation and Feature Extraction Loic Peter Slides pptx
Slides pdf
Exercise 1
October 31, 2012 Principal Component Analysis (PCA) and Statistical Shape Models
(This lecture will exceptionally take place from 12:30 to 14:00)
Diana Mateus, Ahmad Ahmadi Slides pdf Dataset
Exercise Sheet 2
November 7, 2012 Dimensionality Reduction and Manifold Learning Diana Mateus Slides pdf Dataset 1
Dataset 2
Exercise Sheet 3
November 14, 2012 Linear Classifiers and Support Vector Machines (SVMs) Sebastian Pölsterl Slides pdf Code for feature extraction
Exercise Sheet 4
Exercise Sheet 4 IRLS
November 28, 2012 Evaluation Measures and Boostrap Sebastian Pölsterl Slides pdf Exercise Sheet 5
December 5, 2012 Random Forests Olivier Pauly Slides pdf Exercise 6
December 12, 2012 Introduction to OpenCV? + Presentation of Projects David Tan, Stefan Holzer Project description slides
OpenCV Slides
December 19, 2012 Introduction to MevisLab? Abouzar Eslami    
January 16, 2013 Midterm Presentations      
February 6, 2013 Final Presentations      


Group Topic Supervision
Claes Ladefoged
Anne-Claire Morvan
Combining transductive SVMs and active learning Loic
Fausto Milletari
Andrea Catalucci
Cell tracking in time-lapse video sequences Olivier
Markus Zweng
Kamil Neczaj
Catheter segmentation using robust unsupervised learning Diana
Mirije Shefiti
Ergün Kayis
Manifolds of cardiac motion Diana
Vikram Ravindra
Kevin Merckx
Segmentation of knee MR images Olivier

Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Diana Mateus, Olivier Pauly, Loic Peter, Sebastian Pölsterl
Type: Praktikum
Information: 10 ECTS credits
Term: 2012WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.45 - 06 Feb 2013 - 13:16 - DianaMateus

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif