Practical Course:
Machine Learning in Medical Imaging
Nassir Navab, Tingying Peng, Lichao Wang, Loic Peter, Sebastian Poelsterl, Benjamin Gutierrez Becker
Type: Master Practical Course
Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Tuesday 14:00-15:30
Course Language: English
Announcements
- July, 11th: Final presentation are scheduled on 15th of July, 14:00-15:30pm in MI 03.13.010
- May, 28th: Mid-term presentation are rescheduled on 17th of June
- May, 1st: Projects have been assigned. Please contact your supervisor as soon as possible to setup an initial meeting.
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
- 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 by end of February.
- Maximum number of participants: 20.
Requirements
- Basic knowledge in MATLAB and C/C++ programming is an advantage.
Preliminary Schedule
Projects
