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

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI

17 Jan 2024

Abstract excerpt

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and...

Read the complete abstract on PubMed

Topics

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

Share to Bioinformatics and Genomic Networks
Share to Gene expression and cancer classification
Share to Radiomics and Machine Learning in Medical Imaging

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

Source

Identifiers

PMID: 38231805
DOI: 10.1109/jbhi.2024.3355111

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRIPMID 38231805Read ↗

Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public datasetSemantic proximity Read ↗

Decoding Tumor Phenotypes: A Radiologist-Inspired Deep Learning Framework for Breast Cancer Recurrence PredictionSemantic proximity Read ↗

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.Semantic proximity Read ↗

SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural NetworkSemantic proximity Read ↗

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic DataSemantic proximity Read ↗

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integrationSemantic proximity Read ↗

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles.Semantic 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 ↗

Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from HistopathologySemantic proximity Read ↗

Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from HistopathologySemantic proximity Read ↗

Weakly supervised contrastive learning infers molecular subtypes and recurrence of breast cancer from unannotated pathology imagesSemantic proximity Read ↗

Contrastive Learning for Omics-guided Whole-slide Visual Embedding RepresentationSemantic proximity Read ↗

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