1. Machine learning in neuroimaging

2. Instructors

Prof. Dr. Christian Wachinger, Tom Nuno Wolf, Fabian Bongratz, Bailiang Jian, Yitong Li, Emre Kavak

3. Contact

If you have any questions regarding this seminar contact seminars@ai-med.de.

4. Announcements

🏆Best blog posts honorable mention:
The pre-course meeting is on July 9, 2024, at 1pm via Zoom. Zoom link: https://tum-conf.zoom-x.de/j/64817104106?pwd=JbBb2oyY8IcSnILm3f0LUMgVdWekYq.1
Slides from the pre-course meeting: ML-Neuro pre-course July 24.pdf
Slides from the kickoff: ML-Neuro Seminar Winter 24_25_ Kickoff.pdf

5. Registration

Registration to the seminar is done via the TUM Matching Platform. Pay attention to the deadlines!!

6. Timeline

July 9, 2024, 1pm: pre-course meeting
October 18, 2024, 1pm, Seminarraum Holbeinstrasse 11: Kickoff, assignment of papers (attendance is mandatory)
During the semester: meet your supervisor (optional but recommended)
January 8 & 10, Seminarraum Holbeinstrasse 11: Block seminar (attendance is mandatory)

7. Topics

Paper ID	Title	Published in	Link	Group/Supervisor	Student
1	MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant	CVPR 2024	https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_MedM2G_Unifying_Medical_Multi-Modal_Generation_via_Cross-Guided_Diffusion_with_Visual_CVPR_2024_paper.pdf	Yitong	Jiayu Ma
2	Large language models in medicine	Nature Medicine	https://www.nature.com/articles/s41591-023-02448-8	Yitong	Daniel Szabo
3	Fast diffusion-based counterfactuals for shortcut removal and generation.	ECCV 2024	https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/11674.pdf	Yitong	Beatrix Rahnsch
4	Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing	Nature Medicine	https://www.nature.com/articles/s41591-024-02843-9	Bailiang	Derya Dogan
5	AutoRG-Brain: Grounded Report Generation for Brain MRI	Arxiv	https://arxiv.org/abs/2407.16684	Bailiang	Lukas Birner
6	Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation	Arxiv	https://arxiv.org/abs/2407.02235	Bailiang	Gianluca Procopio
7	Diffusion Causal Models for Counterfactual Estimation	CleaR 2022	https://proceedings.mlr.press/v177/sanchez22a/sanchez22a.pdf	Emre	Chenxin Cai
8	Counterfactual Generative Networks.	ICLR 2021	https://openreview.net/pdf?id=BXewfAYMmJw	Emre
9	High Fidelity Image Counterfactuals with Probabilistic Causal Models.	ICML 2023	https://proceedings.mlr.press/v202/de-sousa-ribeiro23a/de-sousa-ribeiro23a.pdf	Emre	Philipp Pindl
10	Merlin: A Vision Language Foundation Model for 3D Computed Tomography	Arxiv	https://arxiv.org/abs/2406.06512	Nuno	Jan Hampe
11	DataDream: Few-shot Guided Dataset Generation	ECCV 2024	https://arxiv.org/abs/2407.10910	Nuno	Martin Zborowski
12	Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs	Medical Image Analysis	https://www.sciencedirect.com/science/article/pii/S1361841524002500	Nuno
13	Diffusion Autoencoders: Toward a Meaningful and Decodable Representation	CVPR 2022	https://openaccess.thecvf.com/content/CVPR2022/papers/Preechakul_Diffusion_Autoencoders_Toward_a_Meaningful_and_Decodable_Representation_CVPR_2022_paper.pdf	Fabi	Milena Schwarz
14	The role of noise in denoising models for anomaly detection in medical images	Medical Image Analysis	https://www.sciencedirect.com/science/article/pii/S1361841523002232	Fabi
15	Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection	MICCAI 2023	https://link.springer.com/chapter/10.1007/978-3-031-43904-9_29	Fabi	Xingyu Xing