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 accepted paper at ICLR 2026. Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the interior volume. Traditional methods treat volumetric and surface-based registration separately, which often leads to inconsistencies that limit downstream analyses. We propose a deep learning framework, UCS, that registers 3D brain MRI images by jointly aligning both cortical and subcortical regions, through a unified volume-and-surface-based representation. Our approach leverages an intermediate spherical coordinate space to bridge anatomical surface topology with volumetric anatomy, enabling consistent and anatomically accurate alignment.

ICLR 2026 Poster accepted paper at ICLR 2026. Self-supervised pre-training has emerged as a critical paradigm for learning transferable representations from unlabeled medical volumetric data. Masked autoencoder based methods have garnered significant attention, yet their application to volumetric medical image faces fundamental limitations from the discrete voxel-level reconstruction objective, which neglects comprehensive anatomical structure continuity. To address this challenge, We propose MedGMAE, a novel framework that replaces traditional voxel reconstruction with 3D Gaussian primitives reconstruction as new perspectives on representation learning. Our approach learns to predict complete sets of 3D Gaussian parameters as semantic abstractions to represent the entire 3D volume, from sparse visible image patches. Code/project link: https://github.com/windrise/MedGMAE; https://anonymous.4open.science/r/MedGMAE-EC8F/

国家医疗保障局公告的 2026 全国医学模拟人和健康传感器智能感知大赛，由国家医保局与湖南省人民政府联合举办，赛道覆盖 AI 虚拟病人、虚拟病人对话模拟、虚拟手术/诊疗系统、护理具身机器人和多类健康传感器智能感知设备。公告要求 2026-05-31 前报名、2026-06-10 前上传参赛项目信息。

京东健康全球医疗 AI 创新大赛公开页面显示赛事聚焦睡眠监测智能算法与医疗大模型创新应用两个方向，面向全球高校、科研机构、企业和个人开放报名，赛程含 6.17-8.7 初赛、后续复赛和 9.21 决赛。

MICCAI Student Board educational challenge for tutorial-style submissions around medical image computing education.

HL7 healthcare AI challenge focused on AI applications around health data interoperability and standards-based workflows.