  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.
