SegRep: Mask-Supervised Learning for Segment Representation in Pathology Images

Abstract

In pathology, various tissue and cell components play diverse biological roles. The morphology of each component can vary markedly with differentiation status or pathological conditions, making it critical for understanding diseases. Traditional computational pathology methods typically employ patch-based feature extraction, which aggregates visual features across entire images. However, this approach does not differentiate between tissue types, limiting component analysis. To address this limitation, we introduce a novel concept in pathology image analysis, namely segment representation learning, and present an algorithm, SegRep, for this purpose. SegRep uses a unique dual-masking strategy that combines input masking and feature map masking. This approach effectively removes external influences for the targeted segment, identified via a segmentation model or manual annotation, allowing for the extraction of segment-specific feature representations. In addition, SegRep utilizes a self-supervised learning algorithm to achieve optimized segment representation. We evaluated SegRep’s efficacy in clustering and classification tasks using a dataset of human gastric cancer samples. The results demonstrate SegRep’s superior capability in extracting feature vectors that are highly specific to different pathology image segments. Compared with traditional methods, SegRep shows significant improvements in accuracy and specificity in both clustering and classification tasks. Segment representations obtained via SegRep can offer a more detailed and insightful perspective on computational pathology, paving the way for advanced applications in the field.

Keywords

• Digital pathology
• feature extraction
• representation learning
• segmentation
• self-supervised learning