Back to search

Article

Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer

2024-03-04

Abstract excerpt

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
b8cb6adb-638a-5b14-ac8f-60812d14fb7e
DOI
10.1101/2024.02.26.582106
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon CancerDOI 10.1101/2024.02.26.582106
Multicenter self-supervised computational pathology identifies prognostic histomorphological phenotypes in colorectal cancerSemantic proximity
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.Semantic proximity
Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learningSemantic proximity
Self-Supervised AI Reveals a Hidden Landscape of Prognostic Spatial Patterns in Multiplex Immunofluorescence ImagesSemantic proximity
Self-Supervised AI Discovery of Histomorphological Phenotypes from Routine Mesothelioma BiopsiesSemantic proximity
Translating Histopathology Foundation Model Embeddings into Cellular and Molecular Features for Clinical StudiesSemantic proximity
Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep LearningSemantic proximity
Prediction of microsatellite instability in colorectal cancer based on tissue phenotypes inferred from pathological whole slide images using self-distillation.Semantic proximity
Image-Based Consensus Molecular Subtyping in Rectal Cancer Biopsies and Response to Neoadjuvant ChemoradiotherapySemantic proximity
A foundation model for generalizable cancer diagnosis and survival prediction from histopathological imagesSemantic proximity
Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2Semantic proximity
AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology imagesSemantic proximity
Select a neighboring publication to make it the new centre.