Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning

TL;DR

This paper introduces DiRL, a method that enhances diversity in attention distribution during self-supervised learning for digital pathology, leading to more comprehensive and context-rich tissue representations.

Contribution

It proposes a cell segmentation-guided dense pretext task to induce attention diversification in SSL models for histopathology images, addressing the limitations of sparsity.

Findings

Attention becomes more globally distributed.

Improved performance on multiple cancer classification tasks.

Enhanced representation richness and diversity.

Abstract

We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models' attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although attention sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological components. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we leverage cell segmentation to densely extract multiple histopathology-specific representations, and then propose a prior-guided dense pretext task for SSL, designed to match the multiple corresponding representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversification in attention for capturing context rich representations. Through quantitative and qualitative analysis on multiple tasks across cancer types, we demonstrate the efficacy of our method and observe that the attention is more globally distributed.