ArticleJournal of imaging2022
HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
- GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images-Simulation of quality control biopsies day.Journal of pathology informatics · 2026Article
- Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches.Journal of pathology informatics · 2026Article
- Lightweight self supervised learning framework for domain generalization in histopathology.Scientific reports · 2025Article
- Retrospective Case-Cohort Study on Risk Factors for Developing Distant Metastases in Women With Breast Cancer.Cancer medicine · 2025Article
- Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.The journal of pathology. Clinical research · 2025Article
- Article
- The next frontier in antibody-drug conjugates: challenges and opportunities in cancer and autoimmune therapy.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.Journal of imaging informatics in medicine · 2024Review
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Predicting HER2 Status Associated with Breast Cancer Aggressiveness Using Four Machine Learning Models.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides.Breast cancer research : BCR · 2024Article
- PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.Bioinformatics (Oxford, England) · 2024Article
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.Diagnostic pathology · 2024Article
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Predicting IHC staining classes of NF1 using features in the hematoxylin channel.Journal of pathology informatics · 2023Article
- Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.Journal of personalized medicine · 2022Review
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin-eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.
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