Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology

Abstract

Accurate and reliable histopathological image classification is essential for breast cancer diagnosis. However, many deep learning models remain sensitive to magnification variability and lack interpretability. To address these challenges, we propose Histo-MExNet, a unified framework designed for scaleinvariant and uncertainty-aware classification. The model integrates DenseNet, ConvNeXt, and EfficientNet backbones within a gated multi-expert architecture, incorporates a prototype learning module for example-driven interpretability, and applies physics-informed regularization to enforce morphology preservation and spatial coherence during feature learning. Monte Carlo Dropout is used to quantify predictive uncertainty. On the BreaKHis dataset, Histo-MExNet achieves 96.97% accuracy under multi-magnification training and demonstrates improved generalization to unseen magnification levels compared to single-expert models, while uncertainty estimation helps identify out-of-distribution samples and reduce overconfident errors, supporting a balanced combination of accuracy, robustness, and interpretability for clinical decision support.

Figures (6)

• Figure 1: Representative subset of the BreaKHis dataset illustrating all eight tumor subtypes across four magnification levels ($40\times$, $100\times$, $200\times$, and $400\times$). Each column stands for a particular form of tumor, and each row stands for a different level of magnification. This shows how tissue morphology and cellular detail change at different scales.
• Figure 2: Overview of the proposed Histo-MExNet framework.Features are extracted using DenseNet201, the most effective backbone, as part with CBAM attention. These features are mapped to a prototype-aligned space and processed by several expert classifiers. The outputs of these classifiers are adaptively fused by a gating network to produce the final prediction.
• Figure 3: t-SNE visualization comparing how well the baseline CNNs and the proposed model using the DenseNet201 backbone separate features at a $100\times$ magnification.
• Figure 4: Comprehensive visual analysis of predictive reliability. Row 1 (a-d): Cross-magnification generalization (trained at $100\times$, evaluated at $40\times$) comparing uncertainty and confidence distributions. Row 2 (e-h): Multi-magnification evaluation comparing variance and predictive confidence (U vs C) correlations for models trained and evaluated on data across all magnification levels.
• Figure 5: Confusion matrix for the DenseNet201 backbone under Type 3 experimental condition, trained on mixed data from all magnifications and assessed on a stratified test split.