ISG: I can See Your Gene Expression

TL;DR

This paper introduces ISG, a novel framework that improves gene expression prediction from high-resolution histology images by focusing on discriminative regions and modeling their interactions, outperforming existing methods.

Contribution

The ISG framework incorporates three new modules—Shannon Selection, Feature Extraction, and Dual Attention—to better utilize texture-rich regions and their interactions for gene prediction.

Findings

ISG significantly outperforms state-of-the-art methods on benchmark datasets.

The Shannon Selection module effectively filters out uninformative regions.

Dual Attention enhances the focus on relevant image regions for gene expression prediction.

Abstract

This paper aims to predict gene expression from a histology slide image precisely. Such a slide image has a large resolution and sparsely distributed textures. These obstruct extracting and interpreting discriminative features from the slide image for diverse gene types prediction. Existing gene expression methods mainly use general components to filter textureless regions, extract features, and aggregate features uniformly across regions. However, they ignore gaps and interactions between different image regions and are therefore inferior in the gene expression task. Instead, we present ISG framework that harnesses interactions among discriminative features from texture-abundant regions by three new modules: 1) a Shannon Selection module, based on the Shannon information content and Solomonoff's theory, to filter out textureless image regions; 2) a Feature Extraction network to extract expressive low-dimensional feature representations for efficient region interactions among a high-resolution image; 3) a Dual Attention network attends to regions with desired gene expression features and aggregates them for the prediction task. Extensive experiments on standard benchmark datasets show that the proposed ISG framework outperforms state-of-the-art methods significantly.