Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghihroohi, Azade Farshad, Yousef Yeganeh
Time: TBA
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.
Announcements
- The presentation and blogpost guidelines are available here: TBA
- The DLMA Introduction slides can be found here: DLMA-PreliminaryMeeting-SS25.pdf
- The preliminary meeting is scheduled for Feb 11st, from 15:30 to 16:00, with the following Zoom link:
https://tum-conf.zoom-x.de/j/62868796969?pwd=hsgvSjqSXCzPnDjSb35vaKMHpG0b6b.1
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, recently published 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 (CVPR, NeurIPS, ICCV, ICLR, ICML, ...).
Course Structure
In this Master Seminar (Hauptseminar), students select one scientific topic from the list provided by course organizers. The students should read the proposed sample papers by the tutors, find the topic-related articles, summarize and compare them in their presentation and blogpost:
- Presentation: The selected paper is presented to the other participants (Maximum 25 minutes presentation, 10 minutes questions). You can use the CAMP templates for PowerPoint TUM-Template.pptx.
- Blog Post: A blog post of 3000-3500 words excluding references, should be submitted before the deadline. The blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated.
- Attendance: Participants have to participate actively in all seminar sessions. Each presentation is followed by a discussion, and everyone is encouraged to actively participate.
Submission Deadline: You have to submit the blog post by the first presentation date and can modify it a bit until the last session of the course.
Schedule
| Date | Session: Topics | Students |
|---|---|---|
| 03/07/2025 | The Role of Synthetic Medical Data in Accelerating AI Healthcare Innovation Embodied AI Solutions in Medical Applications | Amal, Derin Bruns, David |
| 10/07/2025 | Physics Inspired Neural Networks in medical imaging Learning-based prediction of Wall Shear Stress | Lemes Galera, Senad-Leandro Viteri Cuenca, José |
| 17/07/2025 | Video anomaly detection/generation Using Prompt World Modeling Frameworks: From Theory to Implementation in General and Medical Applications Neural and Gaussian representation for Time-of-Flight cameras | Zeck, Konstantin Rayan Siala Jean Jörg Bartholmeß |
List of Topics and Material
The proposed papers for each topic in this course are usually selected from the following venues/publications:
CVPR: Conference on Computer Vision and Pattern Recognition
ICLR: International Conference on Learning Representations
NeurIPS: Neural Information Processing Systems
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)
MICCAI: Medical Image Computing and Computer-Assisted Intervention.
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning
List of topics
| No | Topic | Sample Papers | Journal/ Conference | Tutor | Student | Link |
|---|---|---|---|---|---|---|
| 1 | Video anomaly detection/generation Using Prompt | Generating anomalies for video anomaly detection with prompt-based feature mapping | CVPR 2023 | Zeck, Konstantin | ||
| Learning prompt-enhanced context features for weakly-supervised video anomaly detection | TIP 2024 | https://arxiv.org/pdf/2306.14451 | ||||
| Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection | CVPR 2024 | |||||
| 2 | Physics Inspired Neural Networks in medical imaging | High-resolution hemodynamic estimation from ultrafast ultrasound image velocimetry using a physics-informed neural network. | Physics in Medicine & Biology | Lemes Galera, Senad-Leandro | https://pubmed.ncbi.nlm.nih.gov/39784144/ | |
| A Physics-Informed Neural Network Approach for Determining Spatially Varying Arterial Stiffness Using Ultrasound Imaging: Finite-Difference Simulation and Experimental Plaque-Phantom Validation | IEEE UFFC-JS | https://ieeexplore.ieee.org/document/10794027 | ||||
| Physics-Informed Neural Networks for Transcranial Ultrasound Wave Propagation | Ultrasonics | https://www.sciencedirect.com/science/article/abs/pii/S0041624X23001026 | ||||
| 3 | Embodied AI Solutions in Medical Applications | Embodied intelligence via learning and evolution | Nature | Bruns, David | https://www.nature.com/articles/s41467-021-25874-z | |
| Universal Actions for Enhanced Embodied Foundation Models | CVPR 2025 | https://arxiv.org/pdf/2501.10105 | ||||
| Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks | Arxiv 2024 | https://surgical-robot-transformer.github.io/resources/surgical_robot_transformer.pdf | ||||
| 4 | World Modeling Frameworks: From Theory to Implementation in General and Medical Applications | NVIDIA (PhysicsNeMo, PhysX, Modulus, Newton, Cosmos World Foundation Models) | Rayan Siala | |||
| Google DeepMind (Genie 2, PLATO, Gemini Robotics, MuJoCo) | ||||||
| Microsoft (Aurora, Generative Chemistry Tools) ,... | ||||||
| 5 | Neural and Gaussian representation for Time-of-Flight cameras | Time of the Flight of the Gaussians: Fast and Accurate Dynamic Time-of-Flight Radiance Fields | Jean Jörg Bartholmeß | https://ranrandy.github.io/data/research/2024-11-totfotg_tmp.pdf | ||
| Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields | CVPR 2025 | https://par.nsf.gov/biblio/10580896 | ||||
| ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration | Arxiv 2025 | https://arxiv.org/abs/2504.16545 | ||||
| 6 | Learning-based prediction of Wall Shear Stress | Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach | Computers in Biology and Medicine 2025 | Viteri Cuenca, José | https://www.sciencedirect.com/science/article/pii/S0010482525004883 | |
| Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD | Medical & Biological Engineering & Computing 2025 | https://link.springer.com/article/10.1007/s11517-025-03311-3 | ||||
| WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI | Frontiers in Cardiovascular Medicine 2022 | https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.769927/full | ||||
| 7 | The Role of Synthetic Medical Data in Accelerating AI Healthcare Innovation | Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges | Computers in Biology and Medicine 2025 | Amal, Derin | https://www.sciencedirect.com/science/article/pii/S0010482525001842 | |
| Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis | Nature Machine Intelligence 2023 | https://www.nature.com/articles/s42256-023-00629-1 | ||||
| Self-improving generative foundation model for synthetic medical image generation and clinical applications | Nature Medicine 2024 | https://www.nature.com/articles/s41591-024-03359-y |
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:
- Microsoft Academic Search
- Google Scholar
- CiteSeer
- CiteULike
- Collection of Computer Science Bibliographies
Some publishers:
- ScienceDirect (Elsevier Journals)
- IEEE Journals
- ACM Digital Library
Libraries (online and offline):
- http://rzblx1.uni-regensburg.de/ezeit/ (Elektronische Zeitschriften Bibliothek)
- Verbundkatalog des Bibliotheksverbundes Bayern (BVB)
- Computer ORG
- http://www.ub.tum.de/ (TUM Library)
- To get access onto the electronic library, see http://www.ub.tum.de/medien/ejournals/readme.html
- "proxy.biblio.tu-muenchen.de" mit Port 8080 (nur fuer http). Damit klappen zumindest portal.acm.org und computer.org meistens
- Various proceedings of conferences in our AR-Lab, 03.13.036 (These proceedings are not for lending!)
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.