{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:08:52Z","timestamp":1785409732168,"version":"3.56.0"},"reference-count":28,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"The Scientific and Technological Research Council of T\u00fcrkiye","doi-asserted-by":"publisher","award":["124N419"],"award-info":[{"award-number":["124N419"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"award":["124N419"],"award-info":[{"award-number":["124N419"]}],"id":[{"id":"https:\/\/ror.org\/04w9kkr77","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007504","name":"Istanbul Technical University Scientific Research Projects (ITU BAP) funds","doi-asserted-by":"publisher","award":["44250"],"award-info":[{"award-number":["44250"]}],"id":[{"id":"10.13039\/501100007504","id-type":"DOI","asserted-by":"publisher"}]},{"award":["44250"],"award-info":[{"award-number":["44250"]}],"id":[{"id":"https:\/\/ror.org\/059636586","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: This study developed and evaluated a novel Attention U-Net regression model to assess MLO mammography positioning quality, comparing it against a plain U-Net regression baseline (architectural ablation) and a ResNeXt50 classification baseline. Methods: We curated 1000 patient mammograms (2000 MLO images) from the public VinDr-Mammo dataset, with pectoral muscle line and nipple positions annotated by an expert breast radiologist. Three deep learning models were compared under a 10-fold stratified cross-validation protocol. Statistical significance was assessed with paired Wilcoxon signed-rank tests, McNemar\u2019s test, Cochran\u2019s Q, Wilson score confidence intervals, bootstrap confidence intervals, Holm\u2013Bonferroni correction, and paired Cohen\u2019s d effect sizes. Results: Against the automated PNL reference, the Attention U-Net achieved 85.5% accuracy (Wilson 95% CI [83.9%, 87.0%]), 80.2% sensitivity, and 88.7% specificity, outperforming both the plain U-Net (71.8% accuracy) and ResNeXt50 (73.7% accuracy). (Cochran\u2019s Q p &lt; 0.001; pairwise McNemar p &lt; 0.001). For landmark localization, the Attention U-Net produced significantly lower errors across all five evaluated landmark and geometric measures (nipple 3.44 mm vs. 7.02 mm; perpendicular intersection 5.93 mm vs. 9.81 mm; all comparisons remained significant after Holm\u2013Bonferroni correction). Conclusions: In this single-dataset, single-view study, an Attention U-Net landmark-regression model provided explicit landmark and PNL outputs for quantitative MLO mammography positioning assessment. The model showed improved accuracy and sensitivity compared with a plain U-Net regression baseline and a ResNeXt50 classification baseline.<\/jats:p>","DOI":"10.3390\/diagnostics16142262","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:44:02Z","timestamp":1784544242000},"page":"2262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Landmark Regression with Attention U-Net for Assessing Breast Positioning Quality in MLO Mammography View"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-8872","authenticated-orcid":false,"given":"Nurper","family":"Denizoglu","sequence":"first","affiliation":[{"name":"Department of Radiology, Sultan 2. Abdulhamid Han Training and Research Hospital, Istanbul 34668, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-6880","authenticated-orcid":false,"given":"Toygar","family":"Tanyel","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Graduate Program, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6285-3450","authenticated-orcid":false,"given":"Enes Can","family":"Erkan","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Graduate Program, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-5786","authenticated-orcid":false,"given":"Mustafa Ege","family":"Seker","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, University of Wisconsin, Madison, WI 53792, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3927-321X","authenticated-orcid":false,"given":"Emine","family":"Meltem","sequence":"additional","affiliation":[{"name":"Department of Radiology, Istanbul Training and Research Hospital, University of Health Sciences, Istanbul 34098, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3924-9394","authenticated-orcid":false,"given":"Esma","family":"Aktufan Cerekci","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul 34371, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deniz","family":"Alis","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34684, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilkay","family":"Oksuz","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ercan","family":"Karaarslan","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34684, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erkin","family":"Aribal","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34684, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"ref_1","unstructured":"International Agency for Research on Cancer World Health Organisation (2023, February 19). 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