Hauptseminar/Master Seminar: Deep Learning for Medical Applications
Prof. Nassir Navab
, Dr. Shadi Albarqouni
; Magda Paschali
, Ashkan Khakzar
Advanced Seminar Course Module IN8901
Master Seminar Module IN2107
CAMP Seminar Room, 03.13.010
- 17-07-2019: We would like to encourage you to send us motivation e-mail with title "DLMA_Application" to firstname.lastname@example.org by the 24th of July 2019.
We will only evaluate e-mails that follow the template:
Please do not attach any documents on your motivation e-mails. Thank you!
- Master's program:
- Current Semester:
- Related courses (if passed, mention the grade):
- Short Motivation (Max 3 sentences. It should include related projects/publications/competitions/github repositories):
- 05-07-2019: Preliminary meeting: Thursday, 18.07.2019 (13:00-14:00) in CAMP Seminar Room, 03.13.010.
- 29-06-2019: Website is up!
- Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in the 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: 20.
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.
- Blog Post: A blog post of maximum 1000 words 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 blog post must include all references used and must be written completely in your own words
. Copy and paste will not be tolerated. Both the blog post and presentation have to be done in English
You need to upload your presentation and blog post here
. More details will be provided before the beginning of the semester.
: You have to submit both the presentation and the blog post two weeks
right after your presentation session.
List of Topics and Material
The list of papers:
MICCAI: Medical Image Computing and Computer Assisted Intervention
CVPR: Conference on Computer Vision and Pattern Recognition
ICLR: International Conference on Learning Representations
TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning
NeurIPS: Neural Information Processing Systems
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.