Highlights of SPIE Medical Imaging

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Get ready for the 2024 meeting

The conference where information is shared by leading researchers in image processing, physics, computer-aided diagnosis, perception, image-guided procedures, biomedical applications, ultrasound, informatics, radiology, digital pathology, and much more.

Look over last year's program.

Image highlights from the 2023 San Diego event

The Medical Imaging community is about sharing important research and the latest advancements to help move research and technology into the future. This meeting supports leading researchers doing important work. Check out what's been happening onsite.


Featured 2023 Keynote interviews

Translating computational innovations into reality: Focus on the users!

Elizabeth Krupinski is a professor and vice chair for research in the Department of Radiology and Imaging Sciences at Emory University School of Medicine. She works with medical image perception, covering human factors in radiology.

Federated learning and state-of-the-art brain tumor segmentation models

Prashant Shah, director of Artificial Intelligence for Health and Life Sciences at Intel. He will highlight key considerations in federated learning and discuss the results of the largest international federation of healthcare institutions that developed a brain tumor boundary detection model using MRI scans from 71 institutions across six continents.

Clinical applications of fast, quantitative MR fingerprinting

Dan Ma is an assistant professor in the Department of Biomedical Engineering and the School of Medicine at Case Western Reserve University. Ma will discuss how combining MRF with advanced image analysis techniques could lead to a quantitative imaging tool for cancer imaging and treatment planning.

Human-AI collaboration

Mark Steyvers, professor of cognitive science at the University of California, Irvine, will discuss some of the promises and pitfalls of AI-assisted decision-making, where a human decision-maker is aided by AI.

From code to clinic: The challenges in translating machine-learning models into real-world products

Dale Webster, a research director at Google Health, will share some lessons contrasting a priori expectations (“myths”) with synthesized learnings of what truly transpired (“reality”), to help others who wish to develop and deploy medical AI tools.

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