Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images.

Frontiers in immunology - 1 Jan 2021

Baranwal Mayank, Krishnan Santhoshi, Oneka Morgan, Frankel Timothy, Rao Arvind

Abstract excerpt

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Deep Learning
Share to Models, Theoretical
Share to Pancreatic Diseases

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 34671349
DOI: 10.3389/fimmu.2021.727610
PMCID: PMC8522581

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images.PMID 34671349Read ↗

HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial mapsSemantic proximity Read ↗

UMITIC: An unsupervised framework for the joint characterization of cellular phenotypes and spatial neighborhoods in multiplex and hyperplex immunofluorescence imaging dataSemantic proximity Read ↗

Differentiation of Pancreatic Ductal Adenocarcinoma and Chronic Pancreatitis using Graph Neural Networks on Histopathology and Collagen Fiber FeaturesSemantic proximity Read ↗

Hypergraph-based Multi-instance Contrastive Reinforcement Learning for Annotation-free Pan-cancer Survival Prediction on Whole Slide Histology ImagesSemantic proximity Read ↗

Predictive modelling of highly multiplexed tumour tissue images by graph neural networksSemantic proximity Read ↗

AI inspired discovery of new biomarkers for clinical prognosis of liver cancerSemantic proximity Read ↗

Attention-Enhanced U-Net Segmentation for Reliable Detection of Circulating Tumor-Associated CellsSemantic proximity Read ↗

Attention-Enhanced U-Net Segmentation for Reliable Detection of Circulating Tumor-Associated CellsSemantic proximity Read ↗

A cell-level discriminative neural network model for diagnosis of blood cancersSemantic proximity Read ↗

An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.Semantic proximity Read ↗

Interpretable morphology mapping of peripheral blood leukocytes using annotation-efficient artificial intelligenceSemantic proximity Read ↗

Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.