Yitong Li

Ph.D. Student
Technical University of Munich
Munich Center for Machine Learning (MCML)
Konrad Zuse School of Excellence in Reliable AI (relAI)

About Me

I am a Ph.D. student at the lab for AI in Medical Imaging (AI-Med) in the Technical University of Munich, supervised by Prof. Christian Wachinger. I am affliated with the Munich Center for Machine Learning (MCML) and relAI. Previously, I received the MSc. degree from Biomedical Computing at Technical University of Munich, and BEng. degree from Robot Engineering at Southeast University.

My research passion is in the intersection of computer science and medicine and 3D computer vision, by developing cutting-edge and trustworthy AI tools to solve medical problems, particularly in generative models, self-supervised learning, interpretability and multi-modal learning.

Publications

	DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET Yitong Li, Morteza Ghahremani, Youssef Wally, Christian Wachinger. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. (Oral presentation) [paper] [code]
	Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation Jiajun Wang, Morteza Ghahremani, Yitong Li, Björn Ommer, Christian Wachinger. ( Equal Contribution) Conference on Neural Information Processing Systems (NeurIPS), 2024. [paper] [code]
	PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models Yitong Li, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger. International Conference On Medical Image Computing & Computer Assisted Intervention (MICCAI), 2024. (Early Accept, top 11%) [paper] [code]
	From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data Yitong Li, Tom Nuno Wolf, Sebastian Pölsterl, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger. (* Equal Contribution) Medical Imaging with Deep Learning (MIDL), 2024. (Oral presentation) [paper] [OpenReview] [code]
	On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, Ales Leonardis, Nassir Navab, Benjamin Busam. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [paper] [code]
	Polarimetric Pose Prediction Daoyi Gao, Yitong Li, Patrick Ruhkamp, Iuliia Skobleva, Magdalena Wysock, HyunJun Jung, Pengyuan Wang, Arturo Guridi, Nassir Navab, Benjamin Busam. ( Equal Contribution) European Conference on Computer Vision (ECCV), 2022. [paper] [code]
	CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN Matan Atad, Vitalii Dmytrenko, Yitong Li, Xinyue Zhang, Matthias Keicher, Jan Kirschke, Bene Wiestler, Ashkan Khakzar, Nassir Navab. (* Equal Contribution) ICML 2022 Interpretable Machine Learning in Healthcare Workshop (IMLH), 2022. [paper] [code]
	PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects Pengyuan Wang, HyunJun Jung, Yitong Li, Siyuan Shen, Rahul Parthasarathy Srikanth, Lorenzo Garattoni, Sven Meier, Nassir Navab, Benjamin Busam. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [paper]
	Two-stream 2D/3D Residual Networks for Learning Robot Manipulations from Human Demonstration Videos Xin Xu, Kun Qian, Bo Zhou, Shenghao Chen, Yitong Li. IEEE International Conference on Robotics and Automation (ICRA), 2021. [paper]