Instructors: Prof. Dr. Nassir NavabDr. Shahrooz Faghihroohi, Azade Farshad, Yousef Yeganeh

Registration

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: 

Schedule of Lectures

DateSession: TopicSlidesLecturer
25.05Introduction to Clusters in Garching
Nikolas Brasch




06.07Mid-Presentation
All Students
20.07Invited Talk
Dr. Ahmad Ahmadi from NVIDIA




21.09Final Presentation
All Students


Proposed Projects

ProjectTutorsDescriptionStudents
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
RetinoPathAbril Noguera, Francesca D'Amico, Luisa Ortner, Yiwen Liu
Self-supervised Multimodal Representation LearningDing Zhou, Pei-Ran Huang, Sven Lüpke, Ufuk Demir

Sim2Real OCT Style Transfer

MLMI_Proposal_Sim2Real OCT Style Transfer.pdfQilin Chen, Omar Labib, Mohamed Mokhtar Riahi, Niels Lennart Heissel
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