Hauptseminar/Master Seminar: Deep Learning for Medical Applications
Prof. Nassir Navab
, Shadi Albarqouni
, Christoph Baur
Advanced Seminar Course Module IN8901
Master Seminar Module IN2107
CAMP Seminar Room, 03.13.010
- 18-10-2017: See Requirements (below)!
- 09-10-2017: Papers are assigned!
- 03-10-2017: The pool of papers is ready! Please send us your preferences along with the corresponding ID (at least three preferences) to "email@example.com" by latest 6th October 2017. Please use this title "DLMA_Preferences" when you send your preferences.
- 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 (15:30-16:00) in CAMP Seminar Room, 03.13.010.
- 26-06-2017: Website is up!
- Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in medical imaging community, i.e. IEEE Transaction on Medical Imaging, published recently their special edition on Deep Learning . The Seminar will propose a list of recent scientific articles related to the main current research topics in deep learning for Medical Applications together with some interesting papers from other communities.
- 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: 18.
In this Master Seminar (formerly Hauptseminar), each student is asked to send three preferences from the list, then he will be assigned one paper. In order to successfully complete the seminar, participants have to fulfill these requirements:
- Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
- Written Report: A document of maximum 2 pages should be submitted before the deadline.
- Attendance: Participants have to participate actively in all seminar sessions.
The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The report must include all references used and must be written completely in your own words
. Copy and paste will not be tolerated. Both report and presentation have to be done in English
You need to upload your presentation together with your report here
. Detailed description is written in the Readme
file inside the repository. Please create your account on LRZgit and request an access to the repository.
: You have to submit both the presentation and the written report two weeks
right after your presentation session. Your access to the repository will not be any longer granted after the deadline
List of Topics and Material
The list of papers:
ICML: International Conference on Machine Learning
TMI-SIDL: IEEE Transaction on Medical Imaging, Special Issue on Deep Learning
ISBI: International Symposium on Biomedical Imaging
NIPS: Neural Information Processing Systems
ICRA: International Conference on Robotics and Automation
IJCARS: International Journal on Computer Assisted Radiology and Surgery
JBHI: IEEE Journal of Biomedical and Health Informatics
MICCAI: Medical Image Computing and Computer Assisted Intervention
Literature and Helpful Links
A lot of scientific publications can be found online.
The following list may help you to find some further information on your particular topic:
Libraries (online and offline):
Some further hints for working with references:
If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.
- JabRef is a Java program for comfortable working with Bibtex literature databases. Handy feature: if you know the PubMed ID for an article, JabRef can import data from there (via "Web Search/Medline").
- Mendeley is a cross-platform program for organising your references.