Abstract
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.
Abstract (translated)
癌症诊断通常需要人类病理学家检查组织切片活检整张 slide 图像 (WSIs)，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，最近成为了可行的替代方案。弱监督方法通常使用图像 subsection 或tile 作为输入，并将 WSI 的整体分类基于每个tile的注意力得分。然而，这种方法忽略了可能存在的False positive/negative 机会，因为肿瘤可以是异质的，肿瘤细胞和正常细胞的生长模式比一个tile 更大。这些tile 级别的错误可能导致肿瘤级别的分类错误。为了解决这个问题，我们开发了一种新的深度学习合并操作名为 CHARM(Contrastive Histopathology Attention Resolved Models)。 CHARM 利用 WSI 中单个tile之间的依赖关系，并将其作为多个实例学习模型的先前知识施加环境约束。我们测试了 CHARM 对非小细胞肺癌(NSLC)和淋巴结(LN)转移的分类，结果显示其相比其他弱监督分类算法具有优越性。此外， CHARM 通过可视化注意力区域促进了解释性。
URL
https://arxiv.org/abs/2305.05314
