Laplacian-guided contextual instance learning for whole slide image classification

Classification plays an important role in the diagnosis and prognosis of cancers such as endometrial and breast cancer. Achieving satisfactory performance in classifying cancer molecular subtypes from whole slide images presents a substantial challenge. This difficulty arises from diverse and complex inter-instance relationships and feature homogeneity among different molecular subtypes. To address these issues, this paper presents a novel Laplacian-guided contextual instance learning (LapCIL) framework, which focuses on learning inter-instance relationships to effectively identify molecular subtypes. The LapCIL framework consists of a dynamic contiguous masking strategy, a contextual instance learning block, and a Laplacian channel classification head. In the LapCIL framework, a dynamic contiguous masking strategy is proposed to generate more inter-instance relationships from finite data. Considering the diversity and complexity of inter-instance relationships, a contextual instance learning block is introduced, which leverages a contextual self-attention mechanism to capture the relationships between different instances. To enhance the distinguishing capability between different instances even further, especially in scenarios where feature homogeneity renders it challenging to differentiate morphologically similar cell types, the LapCIL framework incorporates a Laplacian channel classification head. The Laplacian channel classification head focuses on structured local features and dynamically attends to discriminative channel groups. Extensive experiments are conducted on the CAncer MEtastases in LYmphnOdes challeNge (CAMELYON16) breast cancer dataset, the BReAst Carcinoma Subtyping dataset, and a clinical endometrial cancer dataset to evaluate the proposed LapCIL framework. Our framework achieves significant advantages over state-of-the-art methods, both on the clinical dataset and the CAMELYON16 breast cancer dataset.