ARRS: Adaptive Representation and Relevance Scoring Enhance Whole Slide Image Classification using Multi-Instance Learning

The classification of whole slide images (WSIs) is crucial in computational pathology and has significant clinical implications. Due to the extremely high resolution of WSIs and the lack of detailed lesion annotations, Multiple instance learning (MIL) has recently shown great promise for WSI classification by modeling WSIs as "bags" and treating cropped patches as "instances". However, using pre-trained feature extractors often leads to biased instance representations as the data used to pre-train these models differ significantly from histopathology data. Furthermore, since focusing on only certain instances may lead to overlooking important details, it is crucial to comprehensively assess the relevance of all instances for positive instance selection. In this paper, we propose a weakly supervised method to enhance WSI classification using adaptive representation and an instance relevance scoring strategy. To address the issue of biased data representation, we introduce an adaptive representation designed to enhance features relevant to lesion regions. This involves an adaptive block that transforms input features to better represent these critical characteristics, while simultaneously applying an attention-based probability distribution to maintain consistency between the transformed features. Additionally, we propose an instance relevance scoring strategy that assigns importance scores to each instance based on its contribution to the classification. Two publicly available datasets, CAMELYON-16 and TCGA-NSCLC, are used to validate the proposed method. The experimental results show that our proposed method outperforms existing state-of-the-art approaches in WSI classification.