Machine learning in neuroimaging
Instructors
Prof. Dr. Christian Wachinger, Tom Nuno Wolf, Fabian Bongratz, Bailiang Jian, Yitong Li
Contact
If you have any questions regarding this seminar, please contact seminars@ai-med.de.
Announcements
- Pre-Course meeting is on February 6, 2024 at 1pm via Zoom. Zoom link: https://tum-conf.zoom-x.de/j/69409991651?pwd=cnNpbDhkM25GRms0OEFUc3ZGV2Q1QT09, Meeting ID: 694 0999 1651, Passcode: 618434
- Slides from pre-course meeting: ML-Neuro pre-course summer 24.pdf
- This semester, it will be all about Generative Models
- Slides from Kickoff: ML-Neuro Seminar Summer 24 Kickoff.pdf
Registration
Registration to the seminar is done via the TUM Matching Platform. Pay attention to the deadlines!!
Timeline
- February 6, 2024, 1pm: pre-course meeting
- April 4, 2024, 23:59: Deadline for deregistration
- April 23, 2024, 1pm, Seminarraum Holbeinstrasse 11: Kickoff, assignment of papers (attendance is mandatory)
- During the semester: meet your supervisor (optional but recommended)
- June 19 & 21, Seminarraum Holbeinstrasse 11: Block seminar (attendance is mandatory)
Topics
Paper ID | Title | Published in | Link | Group/Supervisor | Student | Additional Material |
---|---|---|---|---|---|---|
1 | Autoencoders | Machine learning for data science handbook | https://arxiv.org/pdf/2003.05991.pdf | AE/Christian Wachinger | Mahir Efe Kaya | |
2 | Masked Autoencoders Are Scalable Vision Learners | CVPR | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf | AE/Christian Wachinger | Katharina Kessler | Medical application: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10230477 |
3 | Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders | IEEE Journal of Biomedical and Health Informatics | https://ieeexplore.ieee.org/abstract/document/8737996 | AE/Christian Wachinger | Lukas Niekerke | |
15 | Deep Variational Autoencoder for Mapping Functional Brain Networks | IEEE Transactions on Cognitive and Developmental Systems | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9204760 | AE/Christian Wachinger | Ekaterina Semenova | |
5 | 3D multi-modality Transformer-GAN for high-quality PET reconstruction | Medical Image Analysis | https://www.sciencedirect.com/science/article/pii/S1361841523002438 | GAN/Nuno Wolf | Matteo Wohlrapp | |
6 | ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization | Medical Image Analysis | https://www.sciencedirect.com/science/article/pii/S1361841523000609 | GAN/Nuno Wolf | Wen-Yen Kuo | |
7 | Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models | Nature Scientific Data | https://www.nature.com/articles/s41597-024-03073-x | GAN/Nuno Wolf | Ali Wali Khan | |
4 | Adding Conditional Control to Text-to-Image Diffusion Models | ICCV | https://arxiv.org/abs/2302.05543 | Diffusion/Fabian Bongratz | Alexandra Samoylova | Related material mentioned in https://github.com/lllyasviel/ControlNet |
8 | Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model | MICCAI | https://arxiv.org/abs/2212.08034 | Diffusion/Fabian Bongratz | Taha Ahmed | |
9 | Denoising Diffusion Probabilistic Models | NeurIPS | https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf | Diffusion/Fabian Bongratz | Rohan Singh | Lots of additional material online, e.g., https://lilianweng.github.io/posts/2021-07-11-diffusion-models/ |
11 | SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation | IPMI | https://arxiv.org/abs/2212.08228 | Diffusion/Yitong Li, Bailiang Jian | Ivan Chzhao | |
12 | DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion | ICCV | https://arxiv.org/abs/2303.06840 | Diffusion/Yitong Li, Bailiang Jian | Boyang ZHONG | |
13 | AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise | CVPR | https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/papers/Wyatt_AnoDDPM_Anomaly_Detection_With_Denoising_Diffusion_Probabilistic_Models_Using_Simplex_CVPRW_2022_paper.pdf | Diffusion/Yitong Li, Bailiang Jian | Ibrahim Canakkaleli |
Resources & Material
Giving talks
Doing a TED Talk: The Full Story
The secret structure of great talks
How to Deliver a Great TED Talk
Blog posts
ML-Neuro Guidelines for blog post