这篇论文用稀疏MoE做视网膜多病分类，专家分配能对应具体病灶，宏AUC到0.912，搞医学影像的可以看看。
视网膜眼底图像常同时存在多种病理，但标准深度学习分类器对所有图像施加静态相同计算。研究者提出新架构，结合引导上下文门控空间注意力和稀疏路由专家混合模块，实现可解释的数据驱动分解。专家分配显著依赖疾病，健康状态和形态学上不同的病理隔离到专属专家。在五类患者分离的5折交叉验证中，模型达到0.912±0.008宏AUC和0.653±0.014宏F1。Grad-CAM++和t-SNE可视化证实专家路由与局部病灶对齐，并将共病案例映射到其组成簇之间。
Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing
Retinal fundus images frequently exhibit multiple co-occurring pathologies, yet standard deep learning classifiers apply static, identical computation to every image regardless of the underlying disease distribution. We propose a novel architecture that resolves this via sparse conditional computation, pairing a Guided Context Gating (GCG) spatial attention front-end with a sparsely-routed Mixture-of-Experts (MoE) block operating over feature tokens. Crucially, this routing yields an interpretable, data-driven decomposition. Expert allocation is significantly disease-dependent (p < 0.001), with the healthy Normal state and morphologically distinct pathologies (e.g., ERM, AMD) isolating to dedicated experts. On a five-class, patient-disjoint 5-fold cross-validation benchmark, our model achieves 0.912 +/- 0.008 macro AUC and 0.653 +/- 0.014 macro F1. Furthermore, Grad-CAM++ and post-MoE t-SNE visualizations confirm that expert routing aligns with localized lesions and geometrically maps co-occurring cases between their constituent clusters, positioning sparse MoE as an interpretable approach to multi-disease retinal screening.