ArticleScientific reports2019
Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational methods, clinical evidence, and translational readiness for artificial intelligence for HER2 assessment using silver in situ hybridization (SISH).Breast cancer research : BCR · 2026Pooled it
- Artificial intelligence enhances cancer cell selection for HER2 gene amplification assessment in breast carcinoma.Virchows Archiv : an international journal of pathology · 2026Article
- HER2 Heterogeneity in Breast Cancer: Biological Basis, Clinical Implications, and Therapeutic Strategies.Breast cancer (Dove Medical Press) · 2026Review
- Multimodal AI reading of genetic complexity and residual marrow composition in acute promyelocytic leukemia.Frontiers in immunology · 2026Article
- One-click reconstruction in single-molecule localization microscopy via experimental parameter-aware deep learning.Npj imaging · 2025Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.Scientific reports · 2024Article
- Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.Diagnostics (Basel, Switzerland) · 2024Review
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Mapping cancer biology in space: applications and perspectives on spatial omics for oncology.Molecular cancer · 2024Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Article
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- Advancements in Focal Amplification Detection in Tumor/Liquid Biopsies and Emerging Clinical Applications.Genes · 2023Review
- Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.Scientific reports · 2023Article
- Pathological identification of HER2-low breast cancer: Tips, tricks, and troubleshooting for the optimal test.Frontiers in molecular biosciences · 2023Review
- Modulating biomolecular condensates: a novel approach to drug discovery.Nature reviews. Drug discovery · 2022Review
- A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis.Cancers · 2022Article
- Singular Nuclei Segmentation for AutomaticSensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.
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