Practical Course:
Machine Learning in Medical Imaging
Nassir Navab,
Lichao Wang,
Tingying Peng,
Diana Mateus,
Loic Peter,
Benjamin Gutierrez Becker,
Shadi Albarqouni,
Fausto Milletarì
Type: Master Practical Course
Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Tuesday 16:00-17:30
Course Language: English
Announcements
- To register for the course, please use the TUM-Matching-System.
- January 21st, 2015: Preliminary meeting was held on January 21st, 15:00-15:30pm in MI 03.13.010.
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 (25%)
- Project (75%), including:
- Practical implementation
- Midterm and final Presentations:Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
- Report : Maximum 8 pages. Please download and use the llncs2e.zip Latex template.
Registration
- Interested students should attend the introductory meeting to enlist in the course.
- Students in the waiting list of summer term would have priority for registration.
- Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached by end of August.
- Maximum number of participants: 20.
Requirements
- Basic knowledge in MATLAB and C/C++ programming is an advantage.
Preliminary Schedule
