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

PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.

Bioinformatics (Oxford, England) - 28 Jun 2024

Yan Chaoyang, Sun Jialiang, Guan Yiming, Feng Jiuxin, Liu Hong, Liu Jian

Abstract excerpt

MOTIVATION: Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover...

Read the complete abstract on PubMed

Topics

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

Share to AI in cancer detection
Share to Breast Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cell Image Analysis Techniques

Share to Computational Biology

Share to HER2/EGFR in Cancer Research

Share to Phenotype

Share to Receptor, ErbB-2

Share to Supervised Machine Learning

Source

Open in PubMed Open full text

Identifiers

PMID: 38940163
DOI: 10.1093/bioinformatics/btae236
PMCID: PMC11211833

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

PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.PMID 38940163Read ↗

Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypesSemantic proximity Read ↗

Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.Semantic proximity Read ↗

Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images.Semantic proximity Read ↗

Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep LearningSemantic proximity Read ↗

Comparison of the classification of HER2 from whole-slide images between pathologists and a deep learning modelSemantic proximity Read ↗

Patch-level phenotype identification via weakly supervised neuron selection in sparse autoencoders for CLIP-derived pathology embeddings.Semantic proximity Read ↗

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

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

Computational pathology infers clinically relevant protein levels and drug response in breast cancer by weakly supervised contrastive learningSemantic proximity Read ↗

Pan-cancer integrative histology-genomic analysis via multimodal deep learningSemantic proximity Read ↗

Interpretable and Robust Deep Learning for Automated HER2 Assessment in Breast CancerSemantic proximity Read ↗

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

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