{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T08:02:54Z","timestamp":1785225774301,"version":"3.55.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110928","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:57:36Z","timestamp":1782853056000},"page":"110928","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Challenges in generalizing mammographic breast density classification using CNNs: Preprocessing, architectures, and cross-dataset analysis"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0298-3480","authenticated-orcid":false,"given":"Rodrigo Leite","family":"Prates","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5880-3242","authenticated-orcid":false,"given":"Wagner","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilfrido","family":"G\u00f3mez-Flores","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110928_b1","series-title":"INCA homepage","author":"Instituto Nacional de C\u00e2ncer (INCA)","year":"2024"},{"key":"10.1016\/j.bspc.2026.110928_b2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s12282-018-0857-5","article-title":"An overview of mammographic density and its association with breast cancer","volume":"25","author":"Nazari","year":"2018","journal-title":"Breast Cancer"},{"key":"10.1016\/j.bspc.2026.110928_b3","series-title":"BI-RADS atlas","author":"American College of Radiology Committee on BI-RADS","year":"2023"},{"issue":"1","key":"10.1016\/j.bspc.2026.110928_b4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1148\/radiol.2018180694","article-title":"Mammographic breast density assessment using deep learning: clinical implementation","volume":"290","author":"Lehman","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.bspc.2026.110928_b5","series-title":"A multi-reconstruction study of breast density estimation using deep learning","author":"Gupta","year":"2022"},{"key":"10.1016\/j.bspc.2026.110928_b6","series-title":"Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing","first-page":"6682","article-title":"Breast density classification with deep convolutional neural networks","author":"Wu","year":"2018"},{"issue":"1","key":"10.1016\/j.bspc.2026.110928_b7","doi-asserted-by":"crossref","first-page":"148","DOI":"10.3390\/app12010148","article-title":"Convolutional neural networks for breast density classification: Performance and explanation insights","volume":"12","author":"Lizzi","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.110928_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108334","article-title":"Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms","volume":"255","author":"L\u00f3pez-\u00dabeda","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"issue":"11","key":"10.1016\/j.bspc.2026.110928_b9","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.3390\/diagnostics14111117","article-title":"Enhancing accuracy in breast density assessment using deep learning: A multicentric, multi-reader study","volume":"14","author":"Biro\u0161","year":"2024","journal-title":"Diagnostics"},{"issue":"5","key":"10.1016\/j.bspc.2026.110928_b10","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1177\/02841851231152097","article-title":"Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model","volume":"64","author":"Lin","year":"2023","journal-title":"Acta Radiol."},{"issue":"2","key":"10.1016\/j.bspc.2026.110928_b11","article-title":"Development and validation of an AI-driven mammographic breast density classification tool based on radiologist consensus","volume":"4","author":"Magni","year":"2022","journal-title":"Radiol.: Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110928_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105489","article-title":"Classification of breast density categories based on SE-Attention neural networks","volume":"193","author":"Deng","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"10.1016\/j.bspc.2026.110928_b13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13058-016-0755-8","article-title":"Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment","volume":"18","author":"Gastounioti","year":"2016","journal-title":"Breast Cancer Res"},{"key":"10.1016\/j.bspc.2026.110928_b14","doi-asserted-by":"crossref","first-page":"18789","DOI":"10.1007\/s11042-016-4340-z","article-title":"A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms","volume":"76","author":"Kumar","year":"2017","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.bspc.2026.110928_b15","series-title":"Medical Imaging 2017: Computer-Aided Diagnosis","article-title":"Feature extraction using convolutional neural network for classifying breast density in mammographic images","volume":"Vol. 10134","author":"Thomaz","year":"2017"},{"key":"10.1016\/j.bspc.2026.110928_b16","series-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016, Lima, Peru, Proceedings","first-page":"101","article-title":"Breast density classification with convolutional neural networks","author":"Fonseca","year":"2017"},{"key":"10.1016\/j.bspc.2026.110928_b17","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3399204","article-title":"A multi-view deep evidential learning approach for mammogram density classification","author":"Gudhe","year":"2024","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.bspc.2026.110928_b18","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.18494\/SAM4826","article-title":"Mammographic breast composition classification using swin transformer network","volume":"36","author":"Tsai","year":"2024","journal-title":"Sensors Mater."},{"issue":"11","key":"10.1016\/j.bspc.2026.110928_b19","doi-asserted-by":"crossref","first-page":"988","DOI":"10.3390\/diagnostics10110988","article-title":"Fully automated breast density segmentation and classification using deep learning","volume":"10","author":"Saffari","year":"2020","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.110928_b20","series-title":"Proc. 10th Int. Conf. on Information Technology in Medicine and Education","first-page":"102","article-title":"Deep learning from small dataset for BI-RADS density classification of mammography images","author":"Shi","year":"2019"},{"issue":"2","key":"10.1016\/j.bspc.2026.110928_b21","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","article-title":"INbreast: Toward a full-field digital mammographic database","volume":"19","author":"Moreira","year":"2012","journal-title":"Academic Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110928_b22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.177","article-title":"A curated mammography data set for use in computer-aided detection and diagnosis research","volume":"4","author":"Lee","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110928_b23","doi-asserted-by":"crossref","unstructured":"C.D. Lekamlage, F. Afzal, E. Westerberg, A. Cheddad, Mini-DDSM: Mammography-based automatic age estimation, in: Proc. 3rd Int. Conf. on Digital Medicine and Image Processing, DMIP, 2020, pp. 1\u20136.","DOI":"10.1145\/3441369.3441370"},{"key":"10.1016\/j.bspc.2026.110928_b24","series-title":"Digital Mammography","first-page":"375","article-title":"The mammographic image analysis society digital mammogram database","volume":"Vol. 1069","author":"Suckling","year":"1994"},{"key":"10.1016\/j.bspc.2026.110928_b25","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41597-023-02430-6","article-title":"A review of the machine learning datasets in mammographic imaging","volume":"10","author":"Logan","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110928_b26","doi-asserted-by":"crossref","first-page":"534","DOI":"10.3390\/bioengineering10050534","article-title":"Exploiting patch sizes and resolutions for multi-scale deep learning in mammogram image classification","volume":"10","author":"Quintana","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.110928_b27","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.bspc.2026.110928_b28","doi-asserted-by":"crossref","unstructured":"C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"10.1016\/j.bspc.2026.110928_b29","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110928_b30","series-title":"EfficientNetV2: Smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.bspc.2026.110928_b31","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"issue":"5","key":"10.1016\/j.bspc.2026.110928_b32","article-title":"RadImageNet: An open radiologic deep learning research dataset for effective transfer learning","volume":"4","author":"Mei","year":"2022","journal-title":"Radiol.: Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110928_b33","doi-asserted-by":"crossref","first-page":"196197","DOI":"10.1109\/ACCESS.2020.3034343","article-title":"Transfer learning with adaptive fine-tuning","volume":"8","author":"Vrban\u010di\u010d","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110928_b34","unstructured":"M. Khaled, D. Gaceb, F. Touazi, A. Otsmane, F. Boutoutaou, Progressive and Combined Deep Transfer Learning for Pneumonia Diagnosis in Chest X-Ray Images, in: Proceedings of IDDM, 2022, pp. 160\u2013173."},{"key":"10.1016\/j.bspc.2026.110928_b35","unstructured":"M. Sukassini, T. Velmurugan, Noise removal using morphology and median filter methods in mammogram images, in: The 3rd International Conference on Small and Medium Business, 2016, pp. 413\u2013419."},{"key":"10.1016\/j.bspc.2026.110928_b36","series-title":"Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, Australia, November 18\u201322, 2019, Proceedings","first-page":"78","article-title":"Deep learning for breast region and pectoral muscle segmentation in digital mammography","author":"Wang","year":"2019"},{"key":"10.1016\/j.bspc.2026.110928_b37","series-title":"2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control","article-title":"Semantic segmentation of mammograms using pre-trained deep neural networks","author":"Prates","year":"2021"},{"issue":"7","key":"10.1016\/j.bspc.2026.110928_b38","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1148\/rg.287085053","article-title":"Digital mammographic artifacts on full-field systems: What are they and how do I fix them?","volume":"28","author":"Ayyala","year":"2008","journal-title":"Radiographics"},{"key":"10.1016\/j.bspc.2026.110928_b39","series-title":"Implementaci\u00f3n de modelos de inteligencia artificial para el c\u00e1lculo de densidad mamaria en im\u00e1genes de mamograf\u00eda","author":"Mora Morales","year":"2024"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426014825?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426014825?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:48:15Z","timestamp":1785224895000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426014825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":39,"alternative-id":["S1746809426014825"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110928","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Challenges in generalizing mammographic breast density classification using CNNs: Preprocessing, architectures, and cross-dataset analysis","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110928","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110928"}}