解决病理图像放大倍数变化带来的分类难题，提升准确率与可解释性。
Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology
- 融合三种主干网络的专家架构，实现跨放大倍数的统一分类。
- 在BreaKHis上达到96.97%准确率，对未见放大倍数泛化能力更强。
- 通过不确定性估计识别异常样本，适合临床辅助诊断场景。
准确可靠的组织病理图像分类对乳腺癌诊断至关重要。然而，许多深度学习模型仍对放大倍数变化敏感且缺乏可解释性。为此，我们提出Histo-MExNet，一种面向尺度不变与不确定性感知分类的统一框架。该模型在门控多专家架构中集成DenseNet、ConvNeXt和EfficientNet主干网络，引入原型学习模块以实现基于实例的可解释性，并采用物理信息正则化，在特征学习过程中保持形态一致性和空间连贯性。通过蒙特卡洛丢弃法量化预测不确定性。在BreaKHis数据集上，Histo-MExNet在多放大倍数训练下实现96.97%的准确率，相较于单专家模型展现出更强的未见放大倍数泛化能力；同时，不确定性估计有助于识别分布外样本，减少过度自信错误，为临床决策支持提供了准确性、鲁棒性与可解释性的平衡方案。
原文摘要 · Abstract (English)
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.
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