ArticleJournal of imaging2019
Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides.
Article in Journal of imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.Breast cancer research : BCR · 2025Article
- Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.NPJ digital medicine · 2025Article
- Interobserver consistency and diagnostic challenges in HER2-ultralow breast cancer: a multicenter study.ESMO open · 2025Article
- Enhancing glioma immunohistochemical image classification through color deconvolution-aware prior guidance.PloS one · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.Cancers · 2024Review
- Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.BME frontiers · 2024Article
- Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment.Journal of digital imaging · 2023Article
- Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques.IEEE journal of translational engineering in health and medicine · 2023Article
- Anti-SARS-CoV-2 equine F (Ab')Scientific reports · 2022Article
- Recombinant BCG expressing the LTAK63 adjuvant improves a short-term chemotherapy schedule in the control of tuberculosis in mice.Frontiers in immunology · 2022Article
- CRISPR/Cas9 Approach to Generate an Auxotrophic BCG Strain for Unmarked Expression of LTAK63 Adjuvant: A Tuberculosis Vaccine Candidate.Frontiers in immunology · 2022Article
- Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning.BME frontiers · 2022Article
- Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.Scientific reports · 2021Article
- HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion.Journal of digital imaging · 2021Article
- Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning.Journal of imaging · 2020Article
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1 author.
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Abstract
The evaluation of breast cancer grades in immunohistochemistry (IHC) slides takes into account various types of visual markers and morphological features of stained membrane regions. Digital pathology algorithms using whole slide images (WSIs) of histology slides have recently been finding several applications in such computer-assisted evaluations. Features that are directly related to biomarkers used by pathologists are generally preferred over the pixel values of entire images, even though the latter has more information content. This paper explores in detail various types of feature measurements that are suitable for the automated scoring of human epidermal growth factor receptor 2 (HER2) in histology slides. These are intensity features known as characteristic curves, texture features in the form of uniform local binary patterns (ULBPs), morphological features specifying connectivity of regions, and first-order statistical features of the overall intensity distribution. This paper considers important properties of the above features and outlines methods for reducing information redundancy, maximizing inter-class separability, and improving classification accuracy in the combined feature set. This paper also presents a detailed experimental analysis performed using the aforementioned features on a WSI dataset of IHC stained slides.
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