PathoDuet：用于 H&E 和 IHC 染色病理切片分析的基础模型
PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
AI总结：
提出病理基础模型 PathoDuet 及其自监督框架，通过跨尺度定位和跨染色迁移任务提升 H&E 与 IHC 多类下游病理分析性能。
摘要：
大量数字化组织病理学数据，为通过自监督学习方法开发病理基础模型展现出广阔前景。使用这些方法预训练的基础模型可作为下游任务的良好基础。然而，自然图像与组织病理学图像之间的差异阻碍了现有方法的直接应用。在本工作中，我们提出了 PathoDuet，这是一系列在组织病理学图像上预训练的模型，也是组织病理学领域一种新的自监督学习框架。该框架的特点是引入了新的预文本 token 和后续任务提升器，以显式利用图像之间的某些关系，例如多种放大倍率和多种染色。基于此，我们设计了两个预文本任务：跨尺度定位和跨染色迁移，分别用于在苏木精和伊红（H&E）图像上预训练模型，以及将模型迁移到免疫组织化学（IHC）图像。为验证模型的有效性，我们在多种下游任务上评估其性能，包括 H&E 领域的图像块级结直肠癌亚型分型和全切片图像（WSI）级分类，以及 IHC 领域的 IHC 标志物表达水平预测、肿瘤识别和切片级定性分析。实验结果表明，我们的模型在大多数任务上具有优越性，并证明了所提出预文本任务的有效性。代码和模型可在 https://github.com/openmedlab/PathoDuet 获取。
英文摘要：
Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
