ReviewPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2021
Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks.
Review in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- From Optical to Molecular Imaging on Human Skin: A Review.Chemical & biomedical imaging · 2026Review
- SlideMamba: entropy-based adaptive fusion of GNN and Mamba for enhanced representation learning in digital pathology.Scientific reports · 2026Article
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Topological data analysis and machine learning for COVID-19 detection in CT scan lung images.BMC biomedical engineering · 2025Article
- Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.NPJ precision oncology · 2024Article
- Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Review
- Graph Neural Network for representation learning of lung cancer.BMC cancer · 2023Article
- Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication.BioData mining · 2023Article
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- Paired-agent imaging as a rapidFrontiers in oncology · 2023Article
- Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.The journal of pathology. Clinical research · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- A graph neural network framework for mapping histological topology in oral mucosal tissue.BMC bioinformatics · 2022Article
- Machine learning techniques on homological persistence features for prostate cancer diagnosis.BMC bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.
Indexed as
Identifiers
33691025PMC7959046What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.