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Organizer: Jiaqi Wang[1], Peirong Liu[2], Can Zhao[3]
[1] Auburn University, [2] Johns Hopkins University, [3] Nvidia
Time: TBD June, 2026
Time: TBD June, 2026
Location: TBD
Slides: TBD
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:
Part 1: Introduction and Background (15min)
Part 2: Physics-informed Learning for Interpretable Medical Imaging (60 min)
Topic 1
Topic 2
Part 3: Open-source Foundation Models for Medical Imaging (60 min)
Topic 1
Topic 2
Coffee Break
Part 4: Collaborative AI for Medical Imaging (60 min)
Topic 1:
Topic 2
Part 5: Conclusion, Future Work, and Q&A (15 min)
Part 6: Panel (30min)
Speakers:
Speakers:
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.
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.
Peirong Liu is currently an Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University. Her research focuses on physics-informed spatiotemporal 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-informed spatiotemporal modeling and representation learning, with applications in medical image analysis and neuroimaging.
Vishal M. Patel is currently an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University. His research focuses on machine learning, computer vision, biometrics, and signal and image processing.
Vishal M. Patel is currently an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University. His research focuses on machine learning, computer vision, biometrics, and signal and image processing.
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.
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