发表机构
Seoul National University; GIST; NVIDIA（首尔国立大学; 韩国科学技术院; 英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全切片图像模型训练难题，提出多源MAE框架ProsMAE，利用多数据集切片预训练编码器，通过ProsCLS用于ISUP分级分类，在不相交PANDA分割下比普通MAE冻结线性探针基线有更高QWK。
AI 中文摘要
全切片图像（WSIs）为计算病理学提供了丰富的诊断信息，但其千兆像素规模、染色变化、扫描仪差异、组织伪影和有限的专家注释使得稳健的模型训练具有挑战性。本文提出了一种名为ProsMAE的多源掩码自动编码器（MAE）框架用于组织病理学表示学习。使用来自前列腺癌分级评估（PANDA）、2017年淋巴结癌转移挑战（CAMELYON17）和乳腺癌亚型（BRACS）的切片进行预训练，将学习到的编码器通过ProsCLS转移用于国际泌尿病理学会（ISUP）分级分类。在评估的不相交PANDA分割下，ProsMAE比普通MAE冻结线性探针基线实现了更高的平均验证二次加权kappa（QWK）。重复分割评估对于进一步建立跨分割组成的稳健性仍然是必要的。
英文摘要
Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning. Tiles from Prostate cANcer graDe Assessment (PANDA), CAncer MEtastases in LYmph nOdes challeNge 2017 (CAMELYON17), and BReAst Carcinoma Subtyping (BRACS) are used for ProsMAE pretraining to expose the encoder to diverse tissue morphology and acquisition conditions. The learned encoder is transferred for International Society of Urological Pathology (ISUP) grade classification through ProsCLS, using a frozen encoder and a linear classification head. ProsMAE achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split. Repeated-split evaluation remains necessary to further establish robustness across split compositions.
CommentsAccepted to APCCAS 2026
