TeachingSs21GDLMA

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Hauptseminar/Master Seminar: Graph Deep Learning for Medical Applications

Prof. Nassir Navab, Anees Kazi, Roger Soberanis, Mahsa Ghorbani

Type: Advanced Seminar Course Module IN0014
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 5 Credits
Location: Due to the pandemic situation, sessions will be online. Access will be shared before each session.
Time: TBA
Course Language: English

Announcements

Introduction

Graph Deep Learning is a new exotic branch in many fields like computer Vision and Medical Imaging. Many real-world medical and non-medical datasets can be represented in the form of graphs, providing a powerful source of information for machine learning models. This graph-based data, combined with the success of the convolutional neural networks, has motivated to translate the key ingredients of deep learning models into the graph domain. Many communities such as healthcare, social media, and computer vision are moving towards analyzing the data using Graph Convolutions. This seminar provides a space for discussion of the recent scientific publications on GCN with a focus on their medical applications and others.

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

Background in Machine/Deep Learning.

List of Topics

TBA

Schedule

Date Time Place Topic Slides
08.02.2021 16:00-16:30 zoom meeting Preliminary meeting slides

Resources and Material

Some introductory material for GCNs:

  • An introductory lecture to GCNs can be found here.

TeachingForm
Title: Graph Deep Learning for Medical Applications
Professor: Prof. Nassir Navab
Tutors: Anees Kazi, Roger Soberanis, Mahsa Ghorbani
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2021SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 12 Feb 2021 - 08:00 - RogerSoberanis

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