arXiv cs.CV· 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)·· 5 小时前AI 评分42
HERO：面向肿瘤学稳健表示的组织学编码器
HERO: Histology Encoder for Robust Representation in Oncology
AI 导读
HERO 是 ViT-G/14 病理基础模型，用 DINO 与 iBOT 目标训练、以高分辨率 Gram anchoring 精调，语料为约 575,000 张临床全切片图像的 5 亿个 tile。公开基准上，它对中心、扫描仪与染色变化的稳健性优于所对比的 SOTA 基础模型，分类、分割和基因表达预测表现相当，并在 39 个切片级临床任务上平均排名第一。
来源：arXiv cs.CV · arxiv.org