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Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Material and methods<\/jats:title>\n                <jats:p>Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub>-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The dataset exhibited a BAC prevalence of 194\/1,493 women (13.0%) and 581\/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs &gt; 0.70 in both training and independent testing subsets. In terms of testing <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub>-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Relevance statement<\/jats:title>\n                <jats:p>Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key points<\/jats:title>\n                <jats:p>\u2022 We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms.<\/jats:p>\n                <jats:p>\u2022 VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts.<\/jats:p>\n                <jats:p>\u2022 Visual explanations using Grad-CAM++ highlighted VGG16\u2019s superior performance in localizing BAC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s41747-024-00478-6","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:01:29Z","timestamp":1721001689000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study"],"prefix":"10.1186","volume":"8","author":[{"given":"Nazanin","family":"Mobini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-4730","authenticated-orcid":false,"given":"Davide","family":"Capra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Colarieti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-8534","authenticated-orcid":false,"given":"Moreno","family":"Zanardo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Baselli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Sardanelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"478_CR1","doi-asserted-by":"crossref","unstructured":"Tsao CW, Aday AW, Almarzooq ZI et al (2023) Heart disease and stroke statistics\u20142023 update: a report from the American Heart Association. 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The other authors declare that they have no competing interests.AC and MZ are members of the Scientific Editorial Board (Section Editor: Abdomen\/gastrointestinal and Section Editor information technology, social media and environmental sustainability, respectively) for <i>European Radiology Experimental.<\/i> They were not involved in the selection nor review processes for this article. In addition, FS is the Editor-in-Chief of the <i>European Radiology Experimental<\/i>; for this reason, he was not involved in any way in the revision\/decision process regarding this manuscript, which was completely managed by the Deputy Editor, Dr. Akos Varga-Szemes (Medical University of South Carolina, Charleston, SC, USA).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"80"}}