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, Tingying Peng, Diana Mateus, Sailesh Conjeti, Abhijit Guha Roy, Anees Kazi

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: 03.13.10
Time: Wednesdays, 10:00-12:00
Course Language: English


  • Since the TUM matching is over and we are currently in the selection process, please do not send us any further motivation letter or inquiry this semester. Students who are interested in the course but missed the matching please consider to participate in the course next semester.
  • Due to the large number of enquiry emails, we are not able to answer all of them. If you would like to register the course, please use TUM matching system, and send a motivation email titled as MLMI motivation plus your name (we will use MLMI motivation as a keyword for email search, so emails with other titles will be automatically ignored), and the content of the letter should be put in the email itself. Please do not attach any additional document like word or pdf, and the letter should be less than 200 words. Due to the limited number of positions, we cannot guarantee a position for every highly-motivated student, so please also consider register the course next semester.
  • Registration is open from 03.02.2017 to 08.02.2017 through the TUM Matching Platform.
  • The preliminary meeting will take place on 25th of Jan at 11:00 a.m. Seminar room 03.13.10
  • Lectures and assignments are password protected
  • To register for the course, please use the TUM-Matching-System

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
25.01.2017 Introduction meeting Tingying Peng LabcourseInfo  
26.04.2017 Introduction to Python Marco Esposito Slides  
03.05.2017 Introduction to machine learning and linear classification Tingying Peng Slides Exercise
17.05.2017 Support Vector Machines Diana Mateus Slides Exercise
Two moons
Digits Dataset
31.05.2017 Random Forests Sailesh Conjeti Slides Exercise and Data
07.06.2017 Deep Learning Christian Rupprecht Slides Exercise
14.06.2017 Dimensionality reduction and clustering Anees Kazi Slides
21.06.2017 Neural Networks and Autoencoders Abhijit Guharoy Slides
Exercise and Data
28.06.2017 Attestation All tutors Timetable  
26.07.2017 Final Presentations All tutors    


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


ID Project Tutor Assigned Student Material
1 Mitochondrial quantification using USK-net Tingying Peng Toker Aysim, Savran Yusuf Mitochondrial Quantification
2 Visualise cell fate choice using dimensional reduction
Tingying Peng Rui Xu, Marilia de Almeida Lirio Cell fate visualisation
3 Nuclear segmentation in TEM images Julien Pontabry Timo Löhr, Luis Hopf Nuclear Segmentation
4 Understanding Spatial Transformer Network (STN) for Classification: Group I Anees Kazi   STN1
5 Understanding Spatial Transformer Network (STN) for Classification: Group II Anees Kazi Mustafa Haha Kocyigit, Haha Emre STN2
6 Detection of abnormality in gastroscopic Images Sailesh Conjeti Laven Kewlani, Amit Pandita EndoVisChallenge I MICCAI 2015
7 Early Barrett's cancer detection
Sailesh Conjeti Vaheh Hatami, Shoaib Khan EndoVisChallenge II MICCAI 2015
8 Predicting Survival of Liver Tumor Patients with Machine Learning Florian Ettlinger Vivek Sethia, Maida Bakovic, Vitaly Nikolaev Survival Prediction
9 Unsupervised Anomaly Detection in Medical Images using GANs Christoph Baur M.Tarek Saban, Wassim Bahaa, Ahmed Ayad Anomaly Detection using GANs
10 CNN-based Pose Regression for Registration
Mai Bui Anshul Sharma, Arvind Somasundaram Pose Regression
11 Infact Brain MRI Segmentation Abhijit Guha Roy Agnieszka Tomczak, Ricardo Smits MICCAI 2017 Challenge
12 Retinal OCT Fluid Segmentation Abhijit Guha Roy Nathalia Spier, Santiago Estrada Grand Challenge

Attestation Schedule


  • 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: 20.


  • *Strong knowledge of MATLAB, Python * or other programment language
  • Background in image processing
  • Exercises will be held in preferably 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: Tingying Peng, Diana Mateus, Sailesh Conjeti, Abhijit Guha Roy, Anees Kazi
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (IN2106)
Term: 2017SoSe

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