ArticleDatabase : the journal of biological databases and curation2022
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.
Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- ConMIL: interactive and contrastive text-guided multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.Neuroinformatics · 2026Article
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- ST2HE: enhancing spatial transcriptomics interpretability via virtual staining for histological annotation.Briefings in bioinformatics · 2026Article
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- Adaptive diagnostic reasoning framework for pathology with multimodal large language models.Communications medicine · 2026Article
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Using spatial proteomics to enhance cell type assignments in histology images.Cell reports methods · 2025Article
- Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Generalized deep learning for histopathology image classification using supervised contrastive learning.Journal of advanced research · 2025Article
- Breast cancer detection based on histological images using fusion of diffusion model outputs.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/.
Indexed as
Identifiers
What OpenQuestion holds
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.