Embedded Files
Organizer: Jiaqi Wang[1], Peirong Liu[2], Can Zhao[3]
Β [1] Auburn University, Β [2] Johns Hopkins University, [3] Nvidia
Time: 8:30 a.m., June 04.
Time: 8:30 a.m., June 04.
Location: ROOM 201
Slides: Coming Soon
Description:
Description:
Recent advances in artificial intelligence (AI) have significantly transformed medical imaging, enabling substantial progress in image acquisition, reconstruction, diagnosis, prognosis, and clinical decision support. Advances in deep learning, foundation models, multimodal integration, and generative modeling have improved accuracy and robustness across modalities such as MRI, CT, X-ray, ultrasound, and digital pathology. Despite these successes, critical challenges remain, including limited generalizability and interpretability, data heterogeneity, privacy concerns, and barriers to real-world clinical deployment. This tutorial provides a concise and up-to-date overview of recent advances in AI for medical imaging, reviewing key paradigms such as physics-informed and interpretable learning, privacy-preserving collaborative learning, and open-source medical imaging foundation models. We further discuss open challenges and future research directions shaping the next generation of medical imaging AI.
Outline:
Outline:
Topic 1: Interpretable AI for Medical Image Understanding and Discovery (60 min)
Multimodal AI for Cancer Discovery from Population-Scale Real-World Data Β ( by Jeya Maria Jose Valanarasu )
From Observed Images to Latent Physiology: Physics-Driven Learning for Interpretable Medical AI ( by Peirong Liu )
Q&A
Topic 2: Open-source Foundation Models for Medical Imaging (60 min)
Topic 3: Collaborative and Privacy Preserving AI for Medical Imaging (60 min)
Federated Medical Foundation Model ( by Jiaqi Wang )
Privacy-preserving video data sharing ( by Sharon Xiaolei Huang )
Q&A
Panel Discussion (15 min)
Sponsorship Talk and Lottery Results (15 min)
Speakers:
Speakers:
Peirong Liu is currently an Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University. Her research focuses on physics-driven learning, generative modeling, and representation learning, with applications in medical image analysis and neuroimaging.
Peirong Liu is currently an Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University. Her research focuses on physics-driven learning, generative modeling, and representation learning, with applications in medical image analysis and neuroimaging.
Jeya Maria Jose Valanarasu is a Senior Researcher at Microsoft Research working on Health AI at scale.Β His current research revolves around building multi-modal AI systems that harness large-scale real-world data to accelerate biomedical discoveries, optimize clinical workflows, and transform healthcare.
Jeya Maria Jose Valanarasu is a Senior Researcher at Microsoft Research working on Health AI at scale.Β His current research revolves around building multi-modal AI systems that harness large-scale real-world data to accelerate biomedical discoveries, optimize clinical workflows, and transform healthcare.
Can Zhao is currently a Senior Research Scientist at NVIDIA focusing on deep learning for medical imaging. Her research focus on medical image generation and foundation models.
Can Zhao is currently a Senior Research Scientist at NVIDIA focusing on deep learning for medical imaging. Her research focus on medical image generation and foundation models.
Yufan He is currently a Senior Research Scientist at NVIDIA focusing on deep learning for medical imaging. His research focus on medical image analysis, vision-language models, robotics and world models.
Yufan He is currently a Senior Research Scientist at NVIDIA focusing on deep learning for medical imaging. His research focus on medical image analysis, vision-language models, robotics and world models.
Sharon Xiaolei Huang is a David Reese Professor in the College of Information Sciences and Technology (IST) at The Pennsylvania State University, where she holds the David Reese Professorship. Her research focuses on Medical AI and computer vision, with contributions to image and video generation, segmentation, multimodal AI, and computer-assisted diagnosis and intervention.Β
Sharon Xiaolei Huang is a David Reese Professor in the College of Information Sciences and Technology (IST) at The Pennsylvania State University, where she holds the David Reese Professorship. Her research focuses on Medical AI and computer vision, with contributions to image and video generation, segmentation, multimodal AI, and computer-assisted diagnosis and intervention.Β
Jiaqi Wang is currently an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. His research focuses on federated learning for healthcare, healthcare informatics, and multimodal medical foundation models.Β
Jiaqi Wang is currently an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. His research focuses on federated learning for healthcare, healthcare informatics, and multimodal medical foundation models.Β
Reading Materials
Reading Materials
Wang, Jiaqi, Xiaochen Wang, Lingjuan Lyu, Jinghui Chen, and Fenglong Ma. "Fedmeki: A benchmark for scaling medical foundation models via federated knowledge injection." Advances in Neural Information Processing Systems 37 (2024): 1082-1116.
Wang, Xiaochen, Jiaqi Wang, Houping Xiao, Jinghui Chen, and Fenglong Ma. "Fedkim: Adaptive federated knowledge injection into medical foundation models." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 8141-8154. 2024.
Wang, Xiaochen, Junyu Luo, Jiaqi Wang, Yuan Zhong, Xiaokun Zhang, Yaqing Wang, Parminder Bhatia, Cao Xiao, and Fenglong Ma. "Unity in diversity: Collaborative pre-training across multimodal medical sources." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3644-3656. 2024.
Wang, Jiaqi, Houping Xiao, and Fenglong Ma. "Asymmetric Mutual Learning for Decentralized Federated Medical Imaging." In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 1-9. 2024.
Wang, Xiaochen, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, and Fenglong Ma. "Hierarchical pretraining on multimodal electronic health records." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 2839-2852. 2023.
Cui, Suhan, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, and Fenglong Ma. "Automated fusion of multimodal electronic health records for better medical predictions." In Proceedings of the... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining, vol. 2024, p. 361. 2024.
Liu, Peirong, Yueh Z. Lee, Stephen R. Aylward, and Marc Niethammer. "Perfusion imaging: an advection diffusion approach." IEEE transactions on medical imaging 40, no. 12 (2021): 3424-3435
Liu, Peirong, Lin Tian, Yubo Zhang, Stephen Aylward, Yueh Lee, and Marc Niethammer. "Discovering hidden physics behind transport dynamics." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10082-10092. 2021.
Liu, Peirong, Yueh Lee, Stephen Aylward, and Marc Niethammer. "Deep decomposition for stochastic normal-abnormal transport." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18791-18801. 2022.
Valanarasu, Jeya Maria Jose, Hanwen Xu, Naoto Usuyama, Chanwoo Kim, Cliff Wong, Peniel Argaw, Racheli Ben Shimol et al. "Multimodal AI generates virtual population for tumor microenvironment modeling." Cell 189, no. 2 (2026): 386-400.
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πππ Please visit our session and enter a $200 gift cardΒ lottery!πππ
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