xMagNet: Dynamic magnification-aware fusion with uncertainty quantification for robust breast cancer histopathology

Abstract

Histopathology image analysis faces challenges due to magnification variability, limiting robust tumor categorization. Existing deep learning models prioritize accuracy but neglect explainability, ethical biases, and real-world deployment. This study proposes xMagNet, a hybrid Transformer-Convolutional Neural Network (CNN) framework that synergizes technical rigor, clinical transparency, and ethical fairness for multi-magnification breast cancer diagnostics. xMagNet integrates a hybrid encoder combining Vision Transformers (ViT) for global tissue modeling at low magnifications (4x - 10x) and Separable Dilation Convolutions (SDC) for localized nuclear texture extraction at high magnifications (20x - 40x). Magnification-Aware Gating (MAG) dynamically balances ViT and SDC features via temperature-scaled sigmoid activation. A multi-task decoder employs Thresholded Grad-CAM (top 10% gradients) for explainable decision-making and Point-wise Reformation Blocks (PRB) for boundary preservation. Federated learning (FL) with momentum-enhanced aggregation and Sinkhorn divergence regularization ensures scanner/stain-invariant training across six institutions (Hamamatsu/Leica, H&E/IHC). Uncertainty-quantified predictions (Monte Carlo dropout) and adversarial debiasing mitigate demographic leakage. xMagNet achieves 97.8% F1-score for tumor segmentation on Camelyon16 and 93% Gleason AUC on PANDA, with 96.5% pathologist concordance via Grad-CAM. At 40x magnification, it detects micro-metastases with 94% sensitivity (vs. UNet++’s 89% and ResUNet’s 91%). Computational efficiency includes sub-second inference (0.42 sec/slide) and 2.3 x faster convergence than HoVer-Net. Ethical auditing reveals 3% fairness gaps ( ) and 73% domain shift reduction (MMD: 0.12 vs. FedAvg’s 0.45), validated on 15,000 whole-slide images (WSIs) from TCGA-BRCA, Camelyon16, and PANDA datasets. xMagNet bridges critical gaps in multi-magnification histopathology by harmonizing technical robustness (MAG fusion, bounded gradients) with clinical utility (HER2+/ER+ subtyping, Gleason grading) and ethical scalability. By achieving high accuracy, rapid inference, and equitable deployment, it advances AI-driven diagnostics toward trustworthy, deployable systems for breast, prostate, and metastatic cancer imaging. Code available at: xMagNet .

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