{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T17:24:38Z","timestamp":1780680278278,"version":"3.54.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":289,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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&amp;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.<\/jats:p>\n               <jats:p>Database URL: https:\/\/www.bracs.icar.cnr.it\/<\/jats:p>","DOI":"10.1093\/database\/baac093","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T18:53:59Z","timestamp":1666032839000},"source":"Crossref","is-referenced-by-count":146,"title":["BRACS: A Dataset for BReAst Carcinoma Subtyping in H&amp;E Histology Images"],"prefix":"10.1093","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9061-1187","authenticated-orcid":false,"given":"Nadia","family":"Brancati","sequence":"first","affiliation":[{"name":"Institute for High Performance Computing and Networking of the Research Council of Italy , 111 Via Pietro Castellino, ICAR-CNR, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Maria","family":"Anniciello","sequence":"additional","affiliation":[{"name":"National Cancer Institute \u2013 IRCCS \u2013 Fondazione Pascale , 53 Via Mariano Semmola, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pushpak","family":"Pati","sequence":"additional","affiliation":[{"name":"IBM Research \u2013 S\u00e4umerstrasse 4, 8803 R\u00fcschlikon , Zurich, Switzerland"},{"name":"ETH, R\u00e4mistrasse 101, 8092 , Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Riccio","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking of the Research Council of Italy , 111 Via Pietro Castellino, ICAR-CNR, Naples 80131, Italy"},{"name":"Department of Electrical Engineering and Information Technologies, Via Claudio, University of Naples Federico II , 21, Naples 80125, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giosu\u00e8","family":"Scognamiglio","sequence":"additional","affiliation":[{"name":"National Cancer Institute \u2013 IRCCS \u2013 Fondazione Pascale , 53 Via Mariano Semmola, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Jaume","sequence":"additional","affiliation":[{"name":"IBM Research \u2013 S\u00e4umerstrasse 4, 8803 R\u00fcschlikon , Zurich, Switzerland"},{"name":"EPFL Rte Cantonale, Lausanne 1015, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"De Pietro","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking of the Research Council of Italy , 111 Via Pietro Castellino, ICAR-CNR, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Di Bonito","sequence":"additional","affiliation":[{"name":"National Cancer Institute \u2013 IRCCS \u2013 Fondazione Pascale , 53 Via Mariano Semmola, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Foncubierta","sequence":"additional","affiliation":[{"name":"IBM Research \u2013 S\u00e4umerstrasse 4, 8803 R\u00fcschlikon , Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"Botti","sequence":"additional","affiliation":[{"name":"National Cancer Institute \u2013 IRCCS \u2013 Fondazione Pascale , 53 Via Mariano Semmola, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Gabrani","sequence":"additional","affiliation":[{"name":"IBM Research \u2013 S\u00e4umerstrasse 4, 8803 R\u00fcschlikon , Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florinda","family":"Feroce","sequence":"additional","affiliation":[{"name":"National Cancer Institute \u2013 IRCCS \u2013 Fondazione Pascale , 53 Via Mariano Semmola, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Frucci","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking of the Research Council of Italy , 111 Via Pietro Castellino, ICAR-CNR, Naples 80131, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"2022110909184599200_R1","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med. Image Anal."},{"key":"2022110909184599200_R2","article-title":"Artificial intelligence and digital pathology: challenges and opportunities","volume":"38","author":"Tizhoosh","year":"2018","journal-title":"J. Pathol. Inf."},{"key":"2022110909184599200_R3","article-title":"Deep neural network models for computational histopathology: a survey","volume":"67","author":"Srinidhi","year":"2020","journal-title":"Med. Image Anal."},{"key":"2022110909184599200_R4","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10050562","article-title":"Machine Learning Methods for Histopathological Image Analysis: a Review","volume":"10","author":"de Matos","year":"2021","journal-title":"Electronics"},{"key":"2022110909184599200_R5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0177544","article-title":"Classification of breast cancer histology images using convolutional neural networks","volume":"12","author":"Ara\u00fajo","year":"2017","journal-title":"PloS One"},{"key":"2022110909184599200_R6","doi-asserted-by":"publisher","first-page":"24680","DOI":"10.1109\/ACCESS.2018.2831280","article-title":"Classification of breast cancer based on histology images using convolutional neural networks","volume":"6","author":"Bardou","year":"2018","journal-title":"IEEE Access"},{"key":"2022110909184599200_R7","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.eswa.2018.09.049","article-title":"Multiple instance learning for histopathological breast cancer image classification","volume":"117","author":"Sudharshan","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"2022110909184599200_R8","first-page":"226","article-title":"Deep computational pathology in breast cancer","author":"Duggento","year":"2020","journal-title":"Semin. Cancer Biol."},{"key":"2022110909184599200_R9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.044","article-title":"BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights","volume":"375","author":"Benhammou","year":"2020","journal-title":"Neurocomputing"},{"key":"2022110909184599200_R10","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/s10278-019-00307-y","article-title":"Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images\u2014a comparative insight","volume":"33","author":"Sharma","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"2022110909184599200_R11","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s12559-020-09813-6","article-title":"Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis","volume":"13","author":"Chugh","year":"2021","journal-title":"Cogn. Comput."},{"key":"2022110909184599200_R12","article-title":"Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits","author":"Asif","year":"2021"},{"key":"2022110909184599200_R13","author":"Janowczyk","year":"2014"},{"key":"2022110909184599200_R14","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"2022110909184599200_R15","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","article-title":"A dataset for breast cancer histopathological image classification","volume":"63","author":"Spanhol","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2022110909184599200_R16","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.media.2019.05.010","article-title":"Bach: grand challenge on breast cancer histology images","volume":"56","author":"Aresta","year":"2019","journal-title":"Med. Image Anal."},{"key":"2022110909184599200_R17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.media.2019.02.012","article-title":"Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge","volume":"54","author":"Veta","year":"2019","journal-title":"Med. Image Anal."},{"key":"2022110909184599200_R18","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1038\/s41374-020-00514-0","article-title":"Artificial intelligence and computational pathology","volume":"101","author":"Cui","year":"2021","journal-title":"Lab. Invest."},{"key":"2022110909184599200_R19","first-page":"116","article-title":"Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations","volume":"8","author":"Wahab","year":"2022","journal-title":"J. Pathol.: Clin. Res."},{"key":"2022110909184599200_R20","doi-asserted-by":"crossref","DOI":"10.1101\/2021.09.24.21263762","article-title":"PathProfiler: automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence, A Case Study for Prostate Cancer Research.","author":"Haghighat","year":"2021"},{"key":"2022110909184599200_R21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"key":"2022110909184599200_R22","article-title":"A pathologist-annotated dataset for validating artificial intelligence: a project description and pilot study","volume":"12","author":"Dudgeon","year":"2021","journal-title":"J. Pathol. Inf."},{"key":"2022110909184599200_R23","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1111\/j.1524-4741.2009.00850.x","article-title":"Flat epithelial atypia and atypical ductal hyperplasia: carcinoma underestimation rate","volume":"16","author":"Frattaruolo","year":"2010","journal-title":"The Breast J."},{"key":"2022110909184599200_R24","volume-title":"WHO Classification of Tumors Editorial Board Lyon (France): international Agency for Research on Cancer 2019","author":"Tan","year":"2010","edition":"5th edn."},{"key":"2022110909184599200_R25","author":"Gonzalez","year":"2022"},{"key":"2022110909184599200_R26","author":"Ara\u00fajo","year":"2018"},{"key":"2022110909184599200_R27","article-title":"Mitosis detection in breast cancer histological images An ICPR 2012 contest","volume":"4","author":"Ludovic","year":"2013","journal-title":"J. Pathol. Inf."},{"key":"2022110909184599200_R28","author":"Polonia","year":"2015"},{"key":"2022110909184599200_R29","author":"Bejnordi","year":"2016"},{"key":"2022110909184599200_R30","author":"Veta","year":"2016"},{"key":"2022110909184599200_R31","author":"Geessink","year":"2017"},{"key":"2022110909184599200_R32","author":"Spanhol","year":"2015"},{"key":"2022110909184599200_R33","article-title":"Radiology data from the cancer genome atlas breast invasive carcinoma [TCGA-BRCA] collection","volume":"10","author":"Lingle","year":"2016","journal-title":"J. Pathol. Inf."},{"key":"2022110909184599200_R34","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1016\/j.cell.2020.10.036","article-title":"Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy","volume":"183","author":"Krug","year":"2020","journal-title":"Cell"},{"key":"2022110909184599200_R35","author":"Brancati","year":"2021"},{"key":"2022110909184599200_R36","first-page":"pp. 167","article-title":"AI slipping on tiles: data leakage in digital pathology","author":"Bussola","year":"2021"},{"key":"2022110909184599200_R37","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-60365-6_20","article-title":"HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification","author":"Pati","year":"2020"},{"key":"2022110909184599200_R38","article-title":"Hierarchical Graph Representations in Digital Pathology","volume-title":"Med. Image Anal.","author":"Pati","year":"2021"},{"key":"2022110909184599200_R39","article-title":"Towards explainable graph representations in digital pathology","author":"Jaume","year":"2020"},{"key":"2022110909184599200_R40","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR46437.2021.00801","article-title":"Quantifying Explainers of Graph Neural Networks in Computational Pathology","author":"Jaume","year":"2021"},{"key":"2022110909184599200_R41","doi-asserted-by":"publisher","first-page":"87552","DOI":"10.1109\/ACCESS.2021.3086892","article-title":"Gigapixel Histopathological Image Analysis Using Attention-Based Neural Networks","volume":"9","author":"Brancati","year":"2021","journal-title":"IEEE Access"},{"key":"2022110909184599200_R42","first-page":"pp. 1107","article-title":"A method for normalizing histology slides for quantitative analysis","author":"Macenko","year":"2009"}],"container-title":["Database"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/baac093\/46880223\/baac093.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/baac093\/46880223\/baac093.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T09:19:06Z","timestamp":1667985546000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/database\/article\/doi\/10.1093\/database\/baac093\/6762252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1093\/database\/baac093","relation":{},"ISSN":["1758-0463"],"issn-type":[{"value":"1758-0463","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1,1]]},"published":{"date-parts":[[2022,1,1]]},"article-number":"baac093"}}