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

CoADS: Cross attention based dual-space graph network for survival prediction of lung cancer using whole slide images.

Computer methods and programs in biomedicine - 1 Jun 2023

Zhao Lu, Hou Runping, Teng Haohua, Fu Xiaolong, Han Yuchen, Zhao Jun

Abstract excerpt

BACKGROUND AND OBJECTIVE: Accurate overall survival (OS) prediction for lung cancer patients is of great significance, which can help classify patients into different risk groups to benefit from personalized treatment. Histopathology slides are considered the gold standard for cancer diagnosis and prognosis, and many algorithms have been proposed to predict the OS risk. Most methods rely on selecting key patches...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Lung Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Neural Networks, Computer

Share to Phenotype

Share to Pulmonary Disease, Chronic Obstructive

Source

Identifiers

PMID: 37119773
DOI: 10.1016/j.cmpb.2023.107559

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

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

CoADS: Cross attention based dual-space graph network for survival prediction of lung cancer using whole slide images.PMID 37119773Read ↗

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survivalSemantic proximity Read ↗

A progressive phased attention model fusing histopathology and genomic features for lung cancer stagingSemantic proximity Read ↗

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomicsSemantic proximity Read ↗

Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.Semantic proximity Read ↗

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE SlidesSemantic proximity Read ↗

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic dataSemantic proximity Read ↗

Graph perceiver network for lung tumor and bronchial premalignant lesion stratification from histopathologySemantic proximity Read ↗

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image.Semantic proximity Read ↗

Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learningSemantic proximity Read ↗

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

Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort studySemantic proximity Read ↗

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics DataSemantic proximity Read ↗

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