{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:44:31Z","timestamp":1782819871343,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A deep learning model with an imbalanced mammography dataset can bias models toward common benign BI-RADS categories and reduce recognition of less frequent malignant or high-risk categories. To address this issue, we propose B2M (Benign Share Benefit to Malignant), a model-agnostic framework for imbalance-aware multi-class BI-RADS classification in C-View mammography. B2M uses a two-phase training strategy that combines dual sampling with feature-space mixing. In Phase I, the model is trained with dual sampling, integrating instance-based and class-balanced sampling to increase minority-class representation while preserving majority-class diversity. In Phase II, the model is fine-tuned with feature-space mixing using samples from the two sampling streams. A soft-target regularization objective supervises the mixed features using labels from both streams, encouraging smoother decision boundaries across BI-RADS categories. We evaluated B2M on an imbalanced mammography cohort from the C-View EMBED dataset using stratified 5-fold cross-validation across multiple CNN backbones. C-View is a synthesized 2D mammographic image generated from 3D digital breast tomosynthesis data, capturing DBT-derived structural information while requiring less memory and computation than processing the full 3D image volume. Among these experiments, ResNeXt-50 with B2M achieved the highest balanced accuracy and Macro-F1 scores compared with the evaluated oversampling and mixing-based methods. This improvement requires an offline training-time overhead of approximately 2.81\u00d7, but it does not increase inference cost. Overall, the results suggest that B2M may be useful for imbalanced multi-class BI-RADS classification in C-View mammography. However, the findings are based on the EMBED cohort, and further validation, including external and prospective evaluation, is needed before clinical use.<\/jats:p>","DOI":"10.3390\/bioengineering13070765","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:26:15Z","timestamp":1782807975000},"page":"765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3656-9107","authenticated-orcid":false,"given":"Farchan Hakim","family":"Raswa","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Fadlurrohman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan"},{"name":"Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bach-Tung","family":"Pham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3306-2035","authenticated-orcid":false,"given":"Ika","family":"Candradewi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4647-5493","authenticated-orcid":false,"family":"Afiahayati","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0633-774X","authenticated-orcid":false,"given":"Ming-Hsiang","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Data Science, Soochow University, Taipei 100006, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5266-2900","authenticated-orcid":false,"given":"Chung-I","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung 402202, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-5491","authenticated-orcid":false,"given":"Kuo-Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chung Yuan Christian University, Taoyuan 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4079-9350","authenticated-orcid":false,"given":"Shih-Lun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan"},{"name":"Research Center for Semiconductor Materials and Advanced Optics, Chung Yuan Christian University, Taoyuan 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-3689","authenticated-orcid":false,"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[{"name":"AI Research Center, Hon Hai Research Institute, Taipei 114065, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","article-title":"Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images","volume":"171","author":"Pranata","year":"2019","journal-title":"Comput. 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