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