arXiv · Computer Vision· Zhi Li (Caris Life Sciences, Irving, TX, United States), Eghbal Amidi (Caris Life Sciences, Irving, TX, United States), Yating Cheng (Caris Life Sciences, Irving, TX, United States), Tyson Dawson (Caris Life Sciences, Irving, TX, United States), Gorkem Can Ates (Caris Life Sciences, Irving, TX, United States), Shuzhen Kuang (Caris Life Sciences, Irving, TX, United States), Norsang Lama (Caris Life Sciences, Irving, TX, United States), Md Ashequr Rahman (Caris Life Sciences, Irving, TX, United States), Zhiying Lu (Caris Life Sciences, Irving, TX, United States), Elisabeth K. Kong (Caris Life Sciences, Irving, TX, United States), Milan Radovich (Caris Life Sciences, Irving, TX, United States), David Spetzler (Caris Life Sciences, Irving, TX, United States), Matthew Oberley (Caris Life Sciences, Irving, TX, United States), George W. Sledge (Caris Life Sciences, Irving, TX, United States), Ming Chen (Caris Life Sciences, Irving, TX, United States)·· 3 小时前AI 评分34
HERO：肿瘤学鲁棒表征的组织学编码器
HERO: Histology Encoder for Robust Representation in Oncology
AI 导读
HERO 是一个基于 ViT-G/14 的病理学基础模型，采用 DINO 和 iBOT 目标训练，并在约 57.5 万例临床全切片图像的 5 亿个图块上进行高分辨率 Gram 锚定微调。在公开基准上，HERO 在中心、扫描仪和染色变化下的鲁棒性最强，39 个切片级临床任务平均排名第一，6 个基准框架的等权综合排名最优。
来源：arXiv · Computer Vision · arxiv.org