TeachingSs2013MLMIPraktikum

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

Practical Course:
Machine Learning in Medical Imaging

Nassir Navab, Diana Mateus, Loic Peter, Sebastian Poelsterl, Pierre Chatelain

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

Announcements

  • For the final presentation each group will have max 7 minutes + 3 minutes for questions
  • Reports are to be handled latest on the 31st of July by email to your supervisor. No extensions will be given
  • Second information meeting will take place in seminar room 03.13.010, on Wednesday the 10th of April at 11:00
  • Shifting the schedule to 10:30-12:00
  • First information meeting will take place in seminar room 03.13.010, on Wednesday the 6th of February at 13:30

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

Evaluation

  • Homework attestations (20%)
  • Practical implementation of the project (50%)
  • Midterm (5%) and final (15%) Presentations:Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
  • Report (10%): Maximum 8 pages. Please download and use the llncs2e.zip Latex template.

Registration

  • Please register through TUMOnline.
  • Maximum number of participants: 16.

Requirements

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

Preliminary Schedule

Date Topic By Slides Homework
09.04.2014 Information Meeting all    
16.04.2014 Image Representation and Feature Extraction Loic Peter    
23.04.2014 Linear Classifiers and Support Vector Machines (SVMs) Sebastian Pölsterl    
30.04.2014 Evaluation Measures and Bootstrap Sebastian Pölsterl    
07.05.2014 Boosting Tingying Peng    
14.05.2014 Random Forests Loic Peter    
28.05.2014 Principal Component Analysis (PCA) and Statistical Shape Models Ahmad Ahmadi    
04.06.2014 Clustering Lichao Wang    
11.06.2014 Midterm Presentations      
12.06.2014 Midterm Presentations      
16.07.2014 Final Presentations      

Project assignments

Group Project Supervision

TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Nassir Navab
Tutors: Diana Mateus, Loic Peter, Sebastian Poelsterl, Pierre Chatelain
Type: Praktikum
Information: 10 ECTS credits
Term: 2013SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.28 - 14 Jan 2014 - 14:47 - TingyingPeng

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