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CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI\u2010RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above\u2010mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD\u00a0systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Acquisition and Validation Methods<\/jats:title>\n                    <jats:p>The BUS dataset comprises\u00a01875 anonymized images from\u00a01064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy\u2010proven tumors divided into\u00a0722 benign and\u00a0342 malignant cases. Besides, a senior ultrasonographer performed a BI\u2010RADS assessment in categories\u00a02 to\u00a05. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5\u2010 and 10\u2010fold cross\u2010validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast\u00a0lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Format and Usage Notes<\/jats:title>\n                    <jats:p>The BUS dataset is publicly available for academic and research purposes through an open\u2010access repository under the name BUS\u2010BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma\u2010Separated Value (CSV)\u00a0files.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Potential Applications<\/jats:title>\n                    <jats:p>The BUS\u2010BRA dataset can be used to develop and assess artificial intelligence\u2010based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI\u2010RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image\u00a0description.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16812","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T07:50:27Z","timestamp":1699429827000},"page":"3110-3123","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":159,"title":["BUS\u2010BRA: A breast ultrasound dataset for assessing computer\u2010aided diagnosis systems"],"prefix":"10.1002","volume":"51","author":[{"given":"Wilfrido","family":"G\u00f3mez\u2010Flores","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n y de Estudios Avanzados del Instituto Polit\u00e9cnico Nacional  Tamaulipas Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Julia","family":"Gregorio\u2010Calas","sequence":"additional","affiliation":[{"name":"Universidade Est\u00e1cio de S\u00e1  Rio de Janeiro Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wagner","family":"Coelho de Albuquerque Pereira","sequence":"additional","affiliation":[{"name":"Programa de Engenharia Biom\u00e9dica\/COPPE Universidade Federal do Rio de Janeiro  Rio de Janeiro Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.10.6049"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2021.03.006"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.13.12072"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.156.3.1899737"},{"key":"e_1_2_9_7_1","volume-title":"ACR BI\u2010RADS Atlas, Breast Imaging Reporting and Data System","author":"D'Orsi CJ","year":"2013"},{"key":"e_1_2_9_8_1","volume-title":"ACR BI\u2010RADS Atlas, Breast Imaging Reporting and Data System","author":"Mendelson E","year":"2013"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0929-6441(08)60005-3"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.02.012"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.600557"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08452-1"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2022.103895"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-03140-5"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08558-0"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2016.0244"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.177"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2731873"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10040729"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.01.032"},{"key":"e_1_2_9_22_1","unstructured":"GeertsmaT.ULTRASOUNDCASES.INFO 2014. 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