Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectively observe specific regions of histopathology tissues under different magnifications, most methods do not incorporate multiple resolutions of the WSIs, hierarchically and attentively, thereby leading to a loss of focus on the WSIs and information from other resolutions. To resolve this issue, we propose a Hierarchical Attention-Guided Multiple Instance Learning framework to fully exploit the WSIs. This framework can dynamically and attentively discover the discriminative regions across multiple resolutions of the WSIs. Within this framework, an Integrated Attention Transformer is proposed to further enhance the performance of the transformer and obtain a more holistic WSI (bag) representation. This transformer consists of multiple Integrated Attention Modules, which is the combination of a transformer layer and an aggregation module that produces a bag representation based on every instance representation in that bag. The experimental results show that our method achieved state-of-the-art performances on multiple datasets, including Camelyon16, TCGA-RCC, TCGA-NSCLC, and an in-house IMGC dataset. The code is available at https://github.com/BearCleverProud/HAG-MIL.

翻译：多实例学习（MIL）和Transformer在组织病理学全切片图像（WSI）分类中日益流行。然而，与人类病理学家在不同放大倍数下选择性地观察组织病理学组织特定区域不同，大多数方法未能以分层且注意力的方式整合WSI的多分辨率信息，导致对WSI的关注焦点缺失及其他分辨率信息的丢失。为解决此问题，我们提出了一种分层注意力引导的多实例学习框架，以充分利用WSI。该框架能够动态且注意力地发现WSI多分辨率中的判别性区域。在该框架内，我们进一步提出了一种集成注意力Transformer，以增强Transformer的性能并获得更全局的WSI（包）表示。该Transformer由多个集成注意力模块组成，每个模块结合了一个Transformer层和一个聚合模块，基于包中每个实例表示生成包表示。实验结果表明，我们的方法在多个数据集上取得了最先进的性能，包括Camelyon16、TCGA-RCC、TCGA-NSCLC以及内部IMGC数据集。代码已开源：https://github.com/BearCleverProud/HAG-MIL。