搜索医学 AI 论文与资源

ICLR 2026 Oral accepted paper at ICLR 2026. In this work, we propose FFDP, a set of IO-aware non-GEMM fused kernels supplemented with a distributed framework for image registration at unprecedented scales. Image registration is an inverse problem fundamental to biomedical and life sciences, but algorithms have not scaled in tandem with image acquisition capabilities. Our framework complements existing model parallelism techniques proposed for large-scale transformer training by optimizing non-GEMM bottlenecks and enabling convolution-aware tensor sharding. We demonstrate unprecedented capabilities by performing multimodal registration of a 100μm ex-vivo human brain MRI volume at native resolution – an inverse problem more than 570× larger than a standard clinical datum in about a minute using only 8 A6000 GPUs.

This Microsoft Learn module helps healthcare professionals adopt Microsoft AI solutions. It covers goals and challenges in life sciences, pharmacology, and healthcare; opportunities for AI; healthcare provider use cases; and business value from AI in patient recovery and health operations.