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

Nassir Navab, Diana Mateus, Sailesh Conjeti, Shadi Albarqouni, Marco Esposito, Pascal Fallavollita

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Time: MONDAYS 10.00 - 12.00
Course Language: English


  • Lectures and assignments are password protected
  • To register for the course, please use the TUM-Matching-System
  • The preliminary meeting will take place on the 25th of January at 13:00 in MI 03.13.010 and will have an approximate duration of 1/2 hour

About the Course

The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided in 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. implementation in Python).
  • The topics of the projects will be distributed in the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realised in couples. The programming language for the projects is to be decided in agreement with your supervisor.


Date Topic By Slides Homework
11.04.2016 Introduction to Python Bring ANACONDA (Python 3) installed in your computer Marco Esposito Course info
Intro to Python
iPython Notebook
18.04.2016 Dimensionality reduction and clustering Shadi Albarqouni PCA
Have fun with faces dataset
25.04.2016 Linear classifiers and evaluation measurements
Presentation of projects: Make your choice before Friday April 29
Tingying Peng LinearClassifier&Evaluation
Project List
02.05.2016 Support Vector Machines Diana Mateus SVM Lecture
Project/Group Assignment
Exercise sheet
Two moons dataset
digits Dataset
09.05.2016 Random Forests Loic Peter Slides Exercise sheet
23.05.2016 Neural Networks and Autoencoders Fausto Milletari Slides Exercise
Additional Material
30.05.2016 Deep Learning Christian Rupprecht Slides Exercise
13.06.2016 Attestation I      
13.06.2016 Attestation II      
18.07.2016 Final Presentations      


The grade will be split between weekly exercises associated to each lecture and a final project.
  • The exercises will be evaluated during a mid-term attestation.
  • The evaluation of the projects will include a final presentation

Attestation Schedule

Student Time
Han Changhee 10:00-11:00
Heinzerling Till 10:00-11:00
Jimenez Sanchez Amelia 10:00-11:00
Lisitsyna Anna 10:00-11:00
Meschede Claus 10:00-11:00
Navarro Avila José 10:00-11:00
Nguyen Thi Yen 10:00-11:00
Nikolaev Vitaly 11:15-12:15
Page Vizcaino Josue 11:15-12:15
Paschali Magdalini 11:15-12:15
Raszl Matheus 11:15-12:15
Rychly Leonard Valentin 11:15-12:15
Schnoy Benjamin 11:15-12:15
Singh Prashant 11:15-12:15


  • Interested students should attend the introductory meeting to enlist in the course.
  • Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached.
  • Maximum number of participants: 16.


  • Strong knowledge of MATLAB or other programming language
  • Strong background in image processing
  • New this year Some of the exercises will be in Python, althoug an introductory lecture on Python will be given previous knowledge would be advantageous.
  • 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.
  • Basic knowledge of Python is advantageous (in case you decide to use python for your final project): numpy, scipy, scikit-learn.
  • Basic knowledge of naive machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...

Title: Machine Learning In Medical Imaging
Professor: Nassir Navab
Tutors: Diana Mateus, Sailesh Conjeti, Shadi Albarqouni, Nicola Rieke; Marco Esposito, Pascal Fallavollita
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2016SoSe

Edit | Attach | Refresh | Diffs | More | Revision r1.33 - 18 Oct 2017 - 19:05 - NicolaRieke

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