Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghih Roohi, Ashkan Khakzar, Azade Farshad, Anees Kazi, Yusef Yegane
- 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.
- The preliminary meeting slides can be found here: MLMISS22.pdf
- The preliminary meeting is scheduled for Feb 3, 10:00 (Zoom link is visible on TUMonline in the course description).
- ُُThe lectures and presentations happen in hybrid form (the meeting info will be shared with participants via email).
- The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture/exercises 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.
In this Master Praktikum (Hauptseminar), students select one scientific article from the list provided by course organizers. The students read the paper, and must accomplish the following:
- 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 (SS2022 MLMI).pdf
- The guideline for the final presentation can be found here: GuidelineMLMI_Final presentation_SS2022.pdf
- Project Progress: 50% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository.
Schedule of Lectures
|12/05||Medical Image Reconstruction||Shahrooz Faghihroohi|
|19/05||Graph Neural Networks||Anees Kazi|
|09/06||Generative Models||Azade Farshad|
|30/06||Invited Talk||Thomas Wendler|
|14/07||Semi-Supervised Methods||Tariq Bdair|
Explaining Medical Image Classifiers with Visual Question Answering Models
Fabian Scherer, Andrei Mancu, Alaeddine Mellouli, Çağhan Köksal
Structured report generation
|MLMI_SoSe22_Structured Report Generation.pdf|
Yiheng Xiong, Jingsong Liu. Priyank Upadhya, Melis Gülenay
A comprehensive study of Semi-Supervised Learning in Medical Imaging
Mert Sayar, Anna Banaszak, Cenk Eralp, Umaid BIn Zubair
SceneGenie: Scene Graph to Image via CLIP Embeddings and Diffusion Model-based Generation
Chengzhi Shen, Yu Chi, Jacopo Sitran, Tobias Vitt
Exploring generative models for OCT Image generation
|MLMI_SoSe22_OCT Image Generation.pdf|
Daria Matiunina, Furkan Çelik, Sebastian Richstein, Murilo Bellatini
GeNoMe: Generating Anomalies in Medical Imaging
Andrea Matécsa, Jakob Ropers, Yixuan Hu, Ata Jadid Ahari