[2604.07298] Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.07298 (cs) [Submitted on 8 Apr 2026] Title:Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification Authors:Xin Tian, Jiuliu Lu, Ephraim Tsalik, Bart Wanders, Colleen Knoth, Julian Knight View a PDF of the paper titled Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification, by Xin Tian and 5 other authors View PDF HTML (experimental) Abstract:Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, current MIL aggregators route all instances through a shared pathway, constraining their capacity to specialise across the pathological heterogeneity inherent in each slide. Mixture-of-Experts (MoE) methods offer a natural remedy by partitioning instances across specialised expert subnetworks; yet unconstrained softmax routing may yield highly imbalanced utilisation, where one or a few experts absorb most routing mass, collapsing the mixture back to a near-single-pathway solution. To address these limitations, we propose ROAM (Region-graph OptimAl-transport Mixture-of-experts), a spatially aware MoE-MIL aggregator that routes region tokens to expert poolers via capacity-constrained entropic optimal transport, promoting balanced expert utilisation by construction. ROAM operates on spatial region tokens, obtained by compressing dense patch bags ...