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.

ICLR 2026 poster introducing AttTok, a medical vision-language method that uses predefined attribute tokens and attribute-centric mechanisms to improve medical image understanding, including classification and visual question answering.

MIMIC-CXR is a large deidentified chest radiograph dataset with associated free-text radiology reports. It is widely used for chest X-ray classification, report generation, image-text representation learning, radiology retrieval, and medical multimodal foundation model evaluation.

SLAKE is a semantically labeled medical visual question answering dataset with bilingual English-Chinese questions, medical images, and knowledge-enhanced annotations. It is useful for medical multimodal learning, image-grounded QA, and radiology VQA evaluation.

CCF-Deadlines lists IEEE BigData 2026 with papers due 2026-08-21 AoE and conference dates 2026-12-14 to 2026-12-17 in Phoenix. IEEE BigData is relevant to healthcare big data, clinical data integration, EHR-scale prediction, biomedical multimodal analytics, and privacy-aware health data mining.