ArticleScientific data2023
A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Longitudinal evaluation of tumor-infiltrating lymphocyte scoring using automated region of interest registration in breast cancer.Breast cancer research : BCR · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin-Stained Whole Slide Images.The American journal of pathology · 2025Article
- Cross-slide augmentation for whole slide image classification based on class activation map.Scientific reports · 2025Article
- Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.Breast cancer research : BCR · 2025Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking.NPJ digital medicine · 2025Review
- The Genomic and Biologic Landscapes of Breast Cancer and Racial Differences.International journal of molecular sciences · 2024Review
- Article
- Quilt-1M: One Million Image-Text Pairs for Histopathology.Advances in neural information processing systems · 2023Article
- A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.Scientific data · 2023Article
- Virtual alignment of pathology image series for multi-gigapixel whole slide images.Nature communications · 2023Article
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.