Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing

• URL: http://arxiv.org/abs/2608.09752v1
• Date: Mon, 10 Aug 2026 15:43:43 GMT
• Title: Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing
• Abstract summary: We propose a novel architecture that resolves this via sparse conditional computation.<n>We show that expert routing aligns with localized lesions and geometrically maps co-occurring cases.
• Score: 3.332219204498619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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