{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:44:11Z","timestamp":1776444251829,"version":"3.51.2"},"reference-count":31,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004881","name":"Mexican Social Security Institute","doi-asserted-by":"crossref","award":["2025-16-4"],"award-info":[{"award-number":["2025-16-4"]}],"id":[{"id":"10.13039\/501100004881","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Council of Humanities, Science and Technology (CONAHCYT, currently SECIHTI), Mexico","award":["1341943"],"award-info":[{"award-number":["1341943"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Breast cancer has been the leading cause of cancer-related deaths among Mexican women since 2006, underscoring the need for improved diagnostic tools and accessible datasets for artificial intelligence (AI). We introduce Mammo-MX, a publicly available large-scale mammography dataset specifically focused on Mexican patients, addressing a critical gap in well-labeled data for deep learning applications. The dataset was acquired between 2023-2024 at the Jalisco Breast Clinic using a HOLOGIC\n                    <jats:sup>\u00ae<\/jats:sup>\n                    Selenia Dimensions full-field digital mammography system, ensuring high-quality imaging. After rigorous curation, Mammo-MX comprises 13,659 mammograms from 3,368 patients with standard craniocaudal (CC) and mediolateral-oblique (MLO) views. Each study was annotated by expert radiologists according to the BI-RADS classification system, and it includes breast density assessments and extensive acquisition metadata. By offering a robust, well-labeled, and freely accessible dataset, Mammo-MX fills a void in current resources, enabling the development of AI models tailored to the Mexican population while also strengthening the global diversity and generalizability of computer-aided diagnostic tools for breast cancer.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ae275c","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T23:47:52Z","timestamp":1764719272000},"page":"040601","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Mammo-MX: an x-ray mammography dataset for computer-aided diagnosis of breast cancer"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-2755","authenticated-orcid":false,"given":"Blanca","family":"Olivia Murillo-Ortiz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7474-9159","authenticated-orcid":true,"given":"Luis","family":"Carlos Padierna","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4342-2059","authenticated-orcid":true,"given":"\u00cd\u00f1igo","family":"Alonso Perea-Campos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8881-5893","authenticated-orcid":true,"given":"Luis","family":"Fernando Parra-S\u00e1nchez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0843-7595","authenticated-orcid":false,"given":"Sergio","family":"Meza-Chavolla","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4921-4195","authenticated-orcid":false,"given":"Samuel","family":"Rivera Rivera","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"mlstae275cbib1","article-title":"Para la prevenci\u00f3n, diagn\u00f3stico, tratamiento, control y vigilancia epidemiol\u00f3gica del c\u00e1ncer de mama (Mexico city: SSA)","author":"Secretar\u00eda de Salud (SSA)","year":"2011","type":"other"},{"key":"mlstae275cbib2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","type":"journal-article","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 Cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-09-26","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}