Cancer diagnoses typically involve human pathologists examining whole slide images (WSIs) of tissue section biopsies to identify tumor cells and their subtypes. However, artificial intelligence (AI)-based models, particularly weakly supervised approaches, have recently emerged as viable alternatives. Weakly supervised approaches often use image subsections or tiles as input, with the overall classification of the WSI based on attention scores assigned to each tile. However, this method overlooks the potential for false positives/negatives because tumors can be heterogeneous, with cancer and normal cells growing in patterns larger than a single tile. Such errors at the tile level could lead to misclassification at the tumor level. To address this limitation, we developed a novel deep learning pooling operator called CHARM (Contrastive Histopathology Attention Resolved Models). CHARM leverages the dependencies among single tiles within a WSI and imposes contextual constraints as prior knowledge to multiple instance learning models. We tested CHARM on the subtyping of non-small cell lung cancer (NSLC) and lymph node (LN) metastasis, and the results demonstrated its superiority over other state-of-the-art weakly supervised classification algorithms. Furthermore, CHARM facilitates interpretability by visualizing regions of attention.

翻译：摘要：癌症诊断通常需要人类病理学家检查组织切片活检的全切片图像（WSI），以识别肿瘤细胞及其亚型。然而，基于人工智能（AI）的模型，特别是弱监督方法，最近已成为可行的替代方案。弱监督方法通常将图像子区域或图块作为输入，并根据分配给每个图块的注意力分数对WSI进行整体分类。然而，这种方法忽略了假阳性/假阴性的可能性，因为肿瘤具有异质性，癌细胞和正常细胞的生长模式可能大于单个图块的尺度。图块层面的此类错误可能导致肿瘤层面的误分类。为解决这一局限，我们开发了一种名为CHARM（对比组织病理学注意力解析模型）的新型深度学习池化算子。CHARM利用WSI内单个图块间的依赖关系，并将上下文约束作为先验知识引入多实例学习模型。我们在非小细胞肺癌（NSCLC）亚型分类和淋巴结（LN）转移检测任务上测试了CHARM，结果表明其优于其他最先进的弱监督分类算法。此外，CHARM通过可视化注意力区域增强了模型的可解释性。