The Whole Pathological Slide Classification via Weakly Supervised Learning

TL;DR

This paper introduces a novel weakly supervised learning framework for whole slide image classification in digital pathology, leveraging pathological priors and contrastive learning to improve cancer detection and subtype differentiation.

Contribution

It proposes a new multi-instance learning framework that incorporates pathological priors, stain separation, and spatial relationships, enhancing WSI classification performance.

Findings

Outperforms state-of-the-art MIL-based methods on Camelyon16 and TCGA-NSCLC datasets.

Effectively detects cancer and differentiates subtypes.

Utilizes pathological priors for improved feature extraction and aggregation.

Abstract

Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis. However, existing methods tend to focus on advanced aggregators with different structures, often overlooking the intrinsic features of H\&E pathological slides. To address this limitation, we introduced two pathological priors: nuclear heterogeneity of diseased cells and spatial correlation of pathological tiles. Leveraging the former, we proposed a data augmentation method that utilizes stain separation during extractor training via a contrastive learning strategy to obtain instance-level representations. We then described the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H\&E-stained tissue images based on pathological inductive bias, encompassing feature extraction, filtering, and aggregation. Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung dataset demonstrate that our proposed framework can effectively handle tasks related to cancer detection and differentiation of subtypes, outperforming state-of-the-art medical image classification methods based on MIL. The code will be released later.