Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11890774 | PMC |
| http://dx.doi.org/10.1038/s41467-025-57541-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
TissueMosaic: Self-supervised learning of tissue representations enables differential spatial transcriptomics across samples.
Cell Syst
September 2025
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:
Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets.
View Article and Find Full Text PDFToward Foundational Model for Sleep Analysis Using a Multimodal Hybrid-Self-Supervised Learning Framework.
IEEE Trans Cybern
September 2025
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective.
View Article and Find Full Text PDFMultimodal self-supervised retinal vessel segmentation.
Neural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFSelf-supervised representation learning with continuous training data improves the feel and performance of myoelectric control.
Comput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFA foundation model for learning genetic associations from brain imaging phenotypes.
Bioinform Adv
August 2025
IBM Research, Yorktown Heights, NY, 10598, United States.
Motivation: Due to the intricate etiology of neurological disorders, finding interpretable associations between multiomics features can be challenging using standard approaches.
Results: We propose COMICAL, a contrastive learning approach using multiomics data to generate associations between genetic markers and brain imaging-derived phenotypes. COMICAL jointly learns omics representations utilizing transformer-based encoders with custom tokenizers.