What question did this study set out to answer?

This research aims to develop a novel ensemble deep learning framework for enhanced breast cancer detection using specialized attention mechanisms.

May 2, 2026

Deep learning-based breast cancer detection with customized ensemble attention.

Key Points

This research aims to develop a novel ensemble deep learning framework for enhanced breast cancer detection using specialized attention mechanisms.
Introduced an ensemble framework combining Multi-Scale Channel Attention, Spatial-Morphological Attention, and Hierarchical Dual Attention for breast pathology analysis.
Utilized three CNN backbones (ResNet-50, DenseNet-121, EfficientNet-B3) augmented with attention modules.
Evaluated performance on benchmark datasets: BreakHis (1,995 images), BACH (400 WSIs with 9,600 patches), and CBIS-DDSM (2,620 cases).
Achieved significant improvements in accuracy: BreakHis (95.6% ± 0.5%), BACH (91.3% ± 0.8%), CBIS-DDSM (93.7% ± 1.0%); p < 0.05, paired t-test.
Individual attention modules contributed to accuracy: MSCA +1.6%, SMA +1.2%, HDA +1.0%.
AI assistance improved pathologist accuracy from 85.4% to 90.2%; p = 0.012.

Abstract

Breast cancer remains one of the leading malignancies globally, and accurate diagnostic decisions at the early stages of the disease can significantly improve patient prognosis. 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× 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 (p < 0.05; 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 ± 0.5% acc), BACH (91.3 ± 0.8% acc), and CBIS-DDSM (93.7 ± 1.0% acc) Ablation studies confirmed the contribution of each attention module (MSCA + 1.6%, SMA + 1.2%, HDA + 1.0%) 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%; p = 0.012). Attention visualizations exhibited clinically meaningful attention overlap with pathologist-annotated ROIs (mean IoU = 0.76) compared to generic CBAM-based attention (IoU = 0.59).

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