{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T10:08:51Z","timestamp":1773742131865,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,17]]},"DOI":"10.7759\/cureus.105360","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:35:31Z","timestamp":1773740131000},"source":"Crossref","is-referenced-by-count":0,"title":["Size-Dependent Performance of Abnormal-Focused U-Net Segmentation for Mammographic Lesion Detection: A Two-Stage Hybrid Training Approach"],"prefix":"10.7759","author":[{"given":"Kian A","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haris K","family":"Choudhary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailey","family":"Mangio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Alkadri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailey  R","family":"Kramer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neelesh S","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","unstructured":"American Cancer Society. Cancer Prevention & Early Detection Facts & Figures. (2025). Accessed: February 1, 2026: https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/cancer-prevention-early-detection.html."},{"key":"ref2","unstructured":"American Cancer Society. Breast Cancer Facts & Figures 2024-2025. (2025). Accessed: February 1, 2026: https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/breast-cancer-facts-figures.html."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2023.0046","article-title":"NCCN guidelines\u00ae insights: breast cancer screening and diagnosis version 1.2023","volume":"21","author":"Bevers TB","year":"2023","unstructured":"Bevers TB, Niell BL, Baker JL, et al.. NCCN guidelines\u00ae insights: breast cancer screening and diagnosis version 1.2023. J Natl Compr Canc Netw. 2023, 21:900-9. 10.6004\/jnccn.2023.0046","journal-title":"J Natl Compr Canc Netw"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.22034\/APJCP.2018.19.2.291","article-title":"Errors in mammography cannot be solved through technology alone","volume":"19","author":"Ekpo EU","year":"2018","unstructured":"Ekpo EU, Alakhras M, Brennan P. Errors in mammography cannot be solved through technology alone. Asian Pac J Cancer Prev. 2018, 19:291-301. 10.22034\/APJCP.2018.19.2.291","journal-title":"Asian Pac J Cancer Prev"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016161174","article-title":"National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium","volume":"283","author":"Lehman CD","year":"2017","unstructured":"Lehman CD, Arao RF, Sprague BL, et al.. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology. 2017, 283:49-58. 10.1148\/radiol.2016161174","journal-title":"Radiology"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2025.04.067","article-title":"Radiologists are human too: addressing the impacts of occupational exhaustion, travel history, and hunger on mammography reading performance","volume":"32","author":"Alshabibi AS","year":"2025","unstructured":"Alshabibi AS, Alhujaili SF, Qenam B, et al.. Radiologists are human too: addressing the impacts of occupational exhaustion, travel history, and hunger on mammography reading performance. Acad Radiol. 2025, 32:4408-20. 10.1016\/j.acra.2025.04.067","journal-title":"Acad Radiol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva A","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017, 542:115-8. 10.1038\/nature21056","journal-title":"Nature"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102481","article-title":"Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation","volume":"66","author":"Soulami KB","year":"2021","unstructured":"Soulami KB, Kaabouch N, Saidi MN, Tamtaoui A. Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control. 2021, 66:8.","journal-title":"Biomed Signal Process Control"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2020.01.006","article-title":"Inconsistent performance of deep learning models on mammogram classification","volume":"17","author":"Wang X","year":"2020","unstructured":"Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. Inconsistent performance of deep learning models on mammogram classification. J Am Coll Radiol. 2020, 17:796-803. 10.1016\/j.jacr.2020.01.006","journal-title":"J Am Coll Radiol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02430-6","article-title":"A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future","volume":"10","author":"Logan J","year":"2023","unstructured":"Logan J, Kennedy PJ, Catchpoole D. A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future. Sci Data. 2023, 10:595. 10.1038\/s41597-023-02430-6","journal-title":"Sci Data"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2147\/BCTT.S550307","article-title":"Exploring AI approaches for breast cancer detection and diagnosis: a review article","volume":"17","author":"Ali A","year":"2025","unstructured":"Ali A, Alghamdi M, Marzuki SS, et al.. Exploring AI approaches for breast cancer detection and diagnosis: a review article. Breast Cancer (Dove Med Press). 2025, 17:927-47. 10.2147\/BCTT.S550307","journal-title":"Breast Cancer (Dove Med Press)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.42343","article-title":"External validation of an ensemble model for automated mammography interpretation by artificial intelligence","volume":"5","author":"Hsu W","year":"2022","unstructured":"Hsu W, Hippe DS, Nakhaei N, et al.. External validation of an ensemble model for automated mammography interpretation by artificial intelligence. JAMA Netw Open. 2022, 5:e2242343. 10.1001\/jamanetworkopen.2022.42343","journal-title":"JAMA Netw Open"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-023-00943-5","article-title":"Robustness of deep networks for mammography: replication across public datasets","volume":"37","author":"Velarde OM","year":"2024","unstructured":"Velarde OM, Lin C, Eskreis-Winkler S, Parra LC. Robustness of deep networks for mammography: replication across public datasets. J Imaging Inform Med. 2024, 37:536-46. 10.1007\/s10278-023-00943-5","journal-title":"J Imaging Inform Med"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01347-9","article-title":"Machine learning based on digital mammography to reduce the need for invasive biopsies of benign calcifications classified in BI-RADS category 4","volume":"38","author":"Wang N","year":"2025","unstructured":"Wang N, Xu W, Wang H, et al.. Machine learning based on digital mammography to reduce the need for invasive biopsies of benign calcifications classified in BI-RADS category 4. J Imaging Inform Med. 2025, 38:2134-45. 10.1007\/s10278-024-01347-9","journal-title":"J Imaging Inform Med"},{"key":"ref15","article-title":"SE-attention U-Net: a hybrid loss-optimized model for small breast lesion segmentation in mammography","volume":"3","author":"Habibi M","year":"2025","unstructured":"Habibi M. SE-attention U-Net: a hybrid loss-optimized model for small breast lesion segmentation in mammography. Res Sq. 2025, 3:6.","journal-title":"Res Sq"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871452","article-title":"Deep learning methods for lesion detection on mammography images: a comparative analysis","volume":"2022","author":"Ribeiro RF","year":"2022","unstructured":"Ribeiro RF, Gomes-Fonseca J, Torres HR, Oliveira B, Vilhena E, Morais P, Vilaca JL. Deep learning methods for lesion detection on mammography images: a comparative analysis. Annu Int Conf IEEE Eng Med Biol Soc. 2022, 2022:3526-9. 10.1109\/EMBC48229.2022.9871452","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab7e7f","article-title":"Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks","volume":"65","author":"Ma X","year":"2020","unstructured":"Ma X, Wang J, Zheng X, et al.. Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks. Phys Med Biol. 2020, 65:105006. 10.1088\/1361-6560\/ab7e7f","journal-title":"Phys Med Biol"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.06865","article-title":"Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network","volume":"1","author":"de Moor T","year":"2018","unstructured":"de Moor T, Rodriguez-Ruiz A, Gubern M\u00e9rida A, Mann R, Teuwen J. Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network. arXiv. 2018, 1:2. 10.48550\/arXiv.1802.06865","journal-title":"arXiv"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.6084\/m9.figshare.24522883.v2","article-title":"Digital mammography dataset for breast cancer diagnosis research (DMID)","volume":"1","author":"Oza P","year":"2023","unstructured":"Oza P, Oza R, Oza U, Sharma P, Patel S, Kumar P, Gohel B. Digital mammography dataset for breast cancer diagnosis research (DMID). Figshare. 2023, 1:4. 10.6084\/m9.figshare.24522883.v2","journal-title":"Figshare"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2025.100416","article-title":"An analytics-driven review of U-Net for medical image segmentation","volume":"8","author":"Neha F","year":"2025","unstructured":"Neha F, Bhati D, Shukla DK, Dalvi SM, Mantzou N, Shubbar S. An analytics-driven review of U-Net for medical image segmentation. Healthc Anal. 2025, 8:6. 10.1016\/j.health.2025.100416","journal-title":"Healthc Anal"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1093\/jncics\/pkaa010","article-title":"Determinants of mammographic breast density by race among a large screening population","volume":"4","author":"Moore JX","year":"2020","unstructured":"Moore JX, Han Y, Appleton C, Colditz G, Toriola AT. Determinants of mammographic breast density by race among a large screening population. JNCI Cancer Spectr. 2020, 4:0. 10.1093\/jncics\/pkaa010","journal-title":"JNCI Cancer Spectr"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/464998-size-dependent-performance-of-abnormal-focused-u-net-segmentation-for-mammographic-lesion-detection-a-two-stage-hybrid-training-approach","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:35:36Z","timestamp":1773740136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/464998-size-dependent-performance-of-abnormal-focused-u-net-segmentation-for-mammographic-lesion-detection-a-two-stage-hybrid-training-approach"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":21,"URL":"https:\/\/doi.org\/10.7759\/cureus.105360","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,17]]}}}