搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Generative models of 3D cardiovascular anatomy can synthesize informative structures for clinical research and medical device evaluation, but face a trade-off between geometric controllability and realism. We propose CardioComposer: a programmable, inference time framework for generating multi-class anatomical label maps from interpretable ellipsoidal primitives. These primitives represent geometric attributes such as the size, shape, and position of discrete substructures. We specifically develop differentiable measurement functions based on voxel-wise geometric moments, enabling loss-based gradient guidance during diffusion model sampling. Code/project link: https://github.com/kkadry/CardioComposer

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2026-05-06. It calls for physics-informed machine learning for personalized disease prediction, prevention, and management, including digital twins, physics-informed generative AI, biomedical time-series, signals, images, interpretability, and clinical decision support.

The Broad Institute ML4H Clinical AI Seminar Series features talks from leading experts at the intersection of AI and medicine. Topics include generative and foundation models, ethical and responsible AI, self-supervised learning, medical imaging, digital twins, and real-world clinical applications.