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This paper introduces a new ensemble deep learning framework, combining three specific attention mechanisms tailored to the breast pathology domain, namely, Multi-Scale Channel Attention (MSCA), Spatial-Morphological Attention (SMA), and Hierarchical Dual Attention (HDA). Unlike generic SENet and CBAM mechanisms, these modules are specifically designed for breast pathology: MSCA captures nuclear-level channel statistics across multiple pooling scales, SMA applies learnable morphological gradient operators to tissue boundary regions, and HDA employs depth-dependent gating to dynamically balance spatial and semantic attention across network stages. While domain-adaptive attention has been explored in medical imaging, the specific combination of multi-scale nuclear statistics, learnable morphological gradients, and depth-adaptive gating tailored jointly to histopathological and mammographic imaging represents a distinct and novel architectural contribution. The framework incorporates three complementary CNN backbones (ResNet-50, DenseNet-121, and EfficientNet-B3) augmented with the proposed attention modules, and intelligently fuses their predictions using a confidence- and performance-based weighted ensemble strategy. Comprehensive experiments were conducted on three publicly available benchmark datasets: the BreakHis histopathology dataset (with 1,995 different images at 40\u00d7 magnification), the BACH challenge dataset (with 400 WSIs producing 9,600 patches), and the CBIS-DDSM mammography dataset (with 2,620 cases). The proposed framework yields statistically significant (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05; paired t-test across five folds) improvements upon individual backbone constituents and surpasses attention-augmented baselines SENet and CBAM by up to 1.8 pp on BreakHis (95.6\u2009\u00b1\u20090.5% acc), BACH (91.3\u2009\u00b1\u20090.8% acc), and CBIS-DDSM (93.7\u2009\u00b1\u20091.0% acc) Ablation studies confirmed the contribution of each attention module (MSCA\u2009+\u20091.6%, SMA\u2009+\u20091.2%, HDA\u2009+\u20091.0%) individually, and a clinical reader study revealed that AI assistance significantly improved the accuracy of pathologists on more challenging cases (accuracy increasing from 85.4 to 90.2%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.012). Attention visualizations exhibited clinically meaningful attention overlap with pathologist-annotated ROIs (mean IoU\u2009=\u20090.76) compared to generic CBAM-based attention (IoU\u2009=\u20090.59).\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-50563-6","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:12:40Z","timestamp":1777486360000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based breast cancer detection with customized ensemble attention"],"prefix":"10.1038","volume":"16","author":[{"given":"Divyakant T.","family":"Meva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalpesh","family":"Popat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirenkumar","family":"Kukadiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikisha","family":"Jariwala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"issue":"1","key":"50563_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel, R. 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