The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention-based multiple instance learning (MIL) models, used for analyzing Whole Slide Images (WSIs) in cancer diagnostics, often overlook the contextual information of tumor and neighboring tiles, leading to misclassifications. To address this, we propose the Context-Aware Multiple Instance Learning (CAMIL) architecture. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a WSI and integrates contextual constraints as prior knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis, achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other state-of-the-art methods. Additionally, CAMIL enhances model interpretability by identifying regions of high diagnostic value.

翻译：组织活检切片的视觉检查是癌症诊断的基础，病理学家通过多倍率分析切片以识别肿瘤细胞及其亚型。然而，现有基于注意力的多实例学习模型在分析癌症诊断中的全切片图像时，往往忽略肿瘤及其相邻切片的上下文信息，导致分类错误。为解决这一问题，我们提出了上下文感知多实例学习（CAMIL）架构。CAMIL引入邻域约束注意力机制以考虑全切片图像内切片之间的依赖关系，并将上下文约束作为先验知识集成到多实例学习模型中。我们在非小细胞肺癌亚型分类（TCGA-NSCLC）和淋巴结转移检测（CAMELYON16）任务上评估了CAMIL，测试AUC分别达到0.959%和0.975%，优于其他现有最先进方法。此外，CAMIL通过识别高诊断价值区域增强了模型的可解释性。