Hauptseminar/Master Seminar: Deep Learning for Medical Applications
Prof. Nassir Navab,
Dr. Seong Tae Kim, Dr. Shahrooz Faghih Roohi,
Azade Farshad,
Ashkan Khakzar
Type: Advanced Seminar Course
Module IN8901
Type: Master Seminar
Module IN2107
SWS: 2+0
ECTS: 5 Credits
*Location: TBA (Due to the pandemic situation we will have presentation sessions online.)
Time: Thursdays, 12-14
Course Language: English
Announcements
- 16-07-2020: Please submit your application here. You also have to register via matching system. Using our application form is not mandatory, but it will help you get a higher ranking in the matching system from our side.
- 30-06-2020: Preliminary meeting: Thursday, 16.07.2020 (12:00-12:30) in virtual meeting room (zoom): Zoom access information is announced in Moodle
- 30-06-2020: Contact information-If you have any question for this seminar, please feel free to contact Dr. Seong Tae Kim (seongtae.kim@tum.de)
- 30-06-2020: Website is up!
Introduction
- 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 [1]. 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.
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
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 1000-1500 words excluding references 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.
Submission Deadline : You have to submit the blog post
two weeks before your presentation session and the presentation one day right after your presentation session.
Schedule
List of Topics and Material
The list of papers:
The paper list and student assignments are announced
here
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:
Some publishers:
Libraries (online and offline):
Some further hints for working with references:
- 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.
If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.
