Practical Course:
Machine Learning in Medical Imaging
Prof. Nassir Navab,
Shadi Albarqouni,
Christoph Baur,
Mai Bui,
Huseyin Coskun,
Abhijit Guha-Roy,
Anees Kazi,
Roger Soberanis
Type: Master Practical Course
Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room,
03.13.010
Time: Mondays, 10-12
Course Language: English
Announcements:
- 16-12-2017: Final Presentation session is moved from 29.01.2018 to 05.02.2018
- 16-12-2017: Our agenda for the intermediate presentation session II is uploaded!
- 17-11-2017: Our agenda for the intermediate presentation session I is uploaded!
- 25-10-2017: Projects are assigned! Please contact your supervisor to kick-off the project.
- 02-10-2017: Lectures are shifted one week! Please double check the schedule!
- 10-07-2017: Please register via TUM Matching System within 14.07.2017 - 19.07.2017 .
- 26-06-2017: An introductory meeting: Monday, 10.07.2017 (16:15-16:45) in CAMP Seminar Room, 03.13.010.
- 26-06-2017: Website is up!
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 at the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realized in couples. The programming language for the projects is to be decided in agreement with your supervisor.
Registration
- Interested students should attend the introductory meeting to enlist in the course.
- Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn't been reached (please pay attention to the Deadlines).
- A maximum number of participants: 20.
Requirements
- Strong knowledge of Python, MATLAB or another programming language
- Background in image processing
- Exercises will be held in preferably in Python, although 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 naive machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
Schedule
Evaluation
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 impact of the exercises on the final grade is 40%.
- The evaluation of the projects will include a final presentation. The impact of the final project on the final grade is 60%.
- Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
Projects
