# Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology Source: [Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology]() Collection index: [llms.txt]() Data updated: 2026-03-18T05:00:11.457Z Authors: Enam Ahmed Taufika, Md Ahasanul Arafatha, Abhijit Kumar Ghoshb, Md. Tanzim Rezab, Md Ashad Alamc Published: 2026-03-15T15:01:00.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Classification on BreaKHis 40X magnification](): Classification; BreaKHis 40X magnification; Accuracy: 98.25. - [Classification on BreaKHis 100X magnification](): Classification; BreaKHis 100X magnification; Accuracy: 96.88. - [Classification on BreaKHis 200X magnification](): Classification; BreaKHis 200X magnification; Accuracy: 92.8. - [Classification on BreaKHis All-Magnification](): Classification; BreaKHis All-Magnification; Accuracy: 96.97. - [Classification on BreaKHis 400X magnification](): Classification; BreaKHis 400X magnification; Accuracy: 88.46. ## Original sources and implementation [Original paper]() [PDF]()