Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghihroohi, Azade Farshad, Yousef Yeganeh
Registration
- Registration must be done through TUM Matching Platform (please pay attention to the Deadlines)
- In order to increase your priority, please also apply via our own Registration system.
- The maximum number of participants: 20-24.
Announcements
- The preliminary meeting slides can be found here:
- The preliminary meeting is scheduled for Feb 2nd, 10:00 (Zoom link is visible on TUMonline in the course description).
- ُُThe mid- and final presentations happen in hybrid form (the meeting info will be shared with participants via email).
Introduction
- The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture block and a project.
- The lectures will include DL topics relevant to medical imaging applications. Each lecture will be followed by a practical hands-on exercise (e.g. the 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.
Course Structure
In this Master Praktikum (Hauptseminar), the grading is divided as follows:
- Presentation: 50% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
- Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip
- The guideline for mid-presentation can be found here: Guideline for intermediate presentation (SS2023 MLMI).pdf
- The guideline for the final presentation can be found here: GuidelineMLMI_Final presentation_SS23.pdf
- Project Progress: 50% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository.
Schedule of Lectures
Date | Session: Topic | Slides | Lecturer |
---|---|---|---|
25.05 | Introduction to Clusters in Garching | Nikolas Brasch | |
06.07 | Mid-Presentation | All Students | |
20.07 | Invited Talk | Dr. Ahmad Ahmadi from NVIDIA | |
21.09 | Final Presentation | All Students |
Proposed Projects
Project | Tutors | Description | Students |
---|---|---|---|
Counterfactual Disease Progression Modelling Using Generative Models | Marta Hasny, Ioannis Charisiadis, Martin Hartenberger, Xiangyu Ning | ||
Hyperspectral CT Reconstruction & Material Segmentation | David Garcia Estrada, Raul David Dominguez Sanchez, Metehan Kaya, Güven Erkaya | ||
Large Language Models for Structuring Radiology Reports | Baris Sozudogru, Abhinav Utkarsh, Zeineb Ben Chaaben, Feng Dang | ||
RetinoPath | Abril Noguera, Francesca D'Amico, Luisa Ortner, Yiwen Liu | ||
Self-supervised Multimodal Representation Learning | Ding Zhou, Pei-Ran Huang, Sven Lüpke, Ufuk Demir | ||
Sim2Real OCT Style Transfer | MLMI_Proposal_Sim2Real OCT Style Transfer.pdf | Qilin Chen, Omar Labib, Mohamed Mokhtar Riahi, Niels Lennart Heissel |