This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly, it introduces a fast patch selection method, FPS, for whole-slide image (WSI) analysis, significantly reducing computational cost while maintaining accuracy. Secondly, it presents PathDino, a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only 9 million parameters, markedly fewer than alternatives. Thirdly, it introduces a rotation-agnostic representation learning paradigm using self-supervised learning, effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets, including both internal datasets spanning four sites (breast, liver, skin, and colorectal) and seven public datasets (PANDA, CAMELYON16, BRACS, DigestPath, Kather, PanNuke, and WSSS4LUAD). Notably, even with a training dataset of 6 million histopathology patches from The Cancer Genome Atlas (TCGA), our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology, rigorously validated through extensive evaluation. Project Page: https://kimialabmayo.github.io/PathDino-Page/

翻译：本文通过三项关键贡献解决了组织病理学图像分析中的复杂挑战。首先，提出了一种快速补丁选择方法FPS，用于全切片图像（WSI）分析，在保持准确性的同时显著降低计算成本。其次，提出了PathDino，这是一种轻量级组织病理学特征提取器，采用最简配置（仅含五个Transformer模块、900万参数），参数数量远低于同类模型。第三，引入了一种基于自监督学习的旋转无关表示学习范式，有效缓解过拟合问题。我们还证明，在12个多样化数据集（涵盖四个内部数据集（乳腺、肝脏、皮肤和结直肠）及七个公开数据集（PANDA、CAMELYON16、BRACS、DigestPath、Kather、PanNuke和WSSS4LUAD））上，我们的紧凑型模型性能超越了现有最先进的组织病理学专用视觉Transformer。值得注意的是，即使仅使用来自癌症基因组图谱（TCGA）的600万张组织病理学补丁进行训练，我们的方法在补丁级别多数投票性能上仍实现了平均8.5%的提升。这些贡献为增强数字病理学图像分析提供了稳健框架，并通过广泛评估得到了严格验证。项目页面：https://kimialabmayo.github.io/PathDino-Page/