{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:44:55Z","timestamp":1787935495552,"version":"build-2784847793"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u0130n\u00f6n\u00fc University BAP","award":["TSA-2026-477"],"award-info":[{"award-number":["TSA-2026-477"]}]},{"award":["TSA-2026-477"],"award-info":[{"award-number":["TSA-2026-477"]}],"id":[{"id":"https:\/\/ror.org\/04asck240","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: This study proposes CMT-BUSNet, a hybrid architecture integrating CNN, Mamba, and Transformer branches for breast ultrasound tumor segmentation with built-in explainability. Methods: CMT-BUSNet employs a CNN-anchored hierarchical parallel encoder where Mamba and Transformer branches process CNN-derived features in parallel, fused through an Adaptive Feature Fusion Module (AFFM) with Dense Nested Decoder and Boundary-Aware Composite Loss. Five-fold cross-validation on BUS-BRA (N = 1875) compared nine architectures under identical protocols, plus nnU-Net v2 trained with its default self-configuring protocol as a benchmark. External evaluation used the BUSI dataset (N = 647). Results: CMT-BUSNet achieved DSC = 0.9037 \u00b1 0.0047 on BUS-BRA with higher boundary delineation metrics than nnU-Net v2, which was trained under a different self-configuring protocol (B-IoU: 0.611 vs. 0.557; HD95: 10.07 vs. 13.54 pixels), despite nnU-Net\u2019s marginally higher DSC (0.9108). On BUSI, CMT-BUSNet (DSC = 0.6709) yielded higher scores than nnU-Net (0.5579) across all metrics under zero-shot transfer, though the two methods were trained under different protocols. Training-based ablation confirmed each component\u2019s contribution, and quantitative XAI validation demonstrated attribution faithfulness (nEAR = 2.82\u00d7) and uncertainty\u2013error correlation (r = 0.39). Conclusions: CMT-BUSNet achieves competitive accuracy with higher boundary metrics, preliminary cross-dataset transferability, and built-in interpretability relative to nnU-Net (noting different training protocols). Internal validation folds are image-disjoint but not guaranteed to be patient-disjoint, which should be considered when interpreting the reported metrics. Multicenter validation is required before clinical deployment.<\/jats:p>","DOI":"10.3390\/diagnostics16081203","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:24:51Z","timestamp":1776435891000},"page":"1203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CMT-BUSNet: Adaptive Fusion-Based Triple-Branch Hybrid Architecture for Explainable Breast Ultrasound Tumor Segmentation"],"prefix":"10.3390","volume":"16","author":[{"given":"H\u00fcseyin","family":"Kutlu","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ad\u0131yaman University, Ad\u0131yaman 02040, T\u00fcrkiye"},{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-098X","authenticated-orcid":false,"given":"Cemil","family":"\u00c7olak","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"705","DOI":"10.3390\/tomography10050055","article-title":"A Review of Artificial Intelligence in Breast Imaging","volume":"10","author":"Helael","year":"2024","journal-title":"Tomography"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.semcancer.2023.09.001","article-title":"Recent Advancements in Artificial Intelligence for Breast Cancer: Image Augmentation, Segmentation, Diagnosis, and Prognosis Approaches","volume":"96","author":"Zhang","year":"2023","journal-title":"Semin. 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