{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:12:47Z","timestamp":1786122767387,"version":"build-2736575974"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate mammographic mass segmentation is crucial for computer-aided breast cancer diagnosis, but remains challenging because masses may be small, low-contrast, irregularly shaped, and partially obscured by dense fibroglandular tissue. Although recent methods have improved contextual representation and multi-scale feature extraction, reliable segmentation still requires better integration of lesion-scale representation, contour-derived auxiliary supervision, and local probability-map refinement. This paper proposes MB-SwinRefiner, a novel two-stage framework consisting of a Multi-scale Boundary-aware SwinUNet (MB-SwinUNet) stage and a probability-guided residual refinement stage. The first stage, MB-SwinUNet, generates an initial mass probability map using hierarchical Swin encoding, multi-scale decoder fusion, and training-only auxiliary boundary supervision. The second stage refines this prediction through probability-guided residual correction by using the Stage 1 probability map as a prior and learning logit-space corrections. Experiments were conducted on a case-level five-fold cross-validation of the INbreast dataset under both a resized benchmark setting and a native sliding-window full-mammogram setting. Performance was evaluated using Dice, intersection over union, sensitivity, and specificity. In the resized benchmark setting, MB-SwinRefiner achieved Dice of 86.19%, IoU of 84.33%, sensitivity of 89.34%, and specificity of 99.98%. In the native sliding-window setting, MB-SwinRefiner improved mean Dice from 76.64% to 78.06% and mean IoU from 74.38% to 76.11%. These results suggest that combining Swin-based contextual encoding, multi-scale decoder fusion, contour-derived training supervision, and probability-guided residual refinement can improve region-based mammographic mass segmentation performance.<\/jats:p>","DOI":"10.3390\/jimaging12080361","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T11:44:53Z","timestamp":1786103093000},"page":"361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MB-SwinRefiner: Mammographic Mass Segmentation with Probability-Guided Residual Refinement"],"prefix":"10.3390","volume":"12","author":[{"given":"Zainab Shanta","family":"Swayedi","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1300-7848","authenticated-orcid":false,"given":"Pedram","family":"Salehpour","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-7370","authenticated-orcid":false,"given":"Hadi","family":"Aghdasi","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1038\/bjc.2013.177","article-title":"The benefits and harms of breast cancer screening: An independent review","volume":"108","author":"Marmot","year":"2013","journal-title":"Br. 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