The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"

<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (&ldquo;DX&rdquo; on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to &ldquo;Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer&rdquo; these histology images were categorized into The histology images were classified as &ldquo;MSS&rdquo; (microsatellite stable) or &ldquo;MSIMUT&rdquo; (microsatellite unstable or highly mutated) according to &ldquo;Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer&rdquo;, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>