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

arXiv

View on arXiv

4.0/10

Platform Domination RiskN/A

Market Consolidation RiskN/A

Displacement HorizonN/A

CORE FUNCTION

Implements a Mixture-of-Experts (MoE) framework for gigapixel whole-slide image classification using Optimal Transport and region-graph-based routing to handle pathological heterogeneity.

TRACTION

citations

0.0 velocity

co_authors

0.0 velocity

REASONING

The project introduces a sophisticated routing mechanism for MIL in pathology by combining MoE with Optimal Transport. While technically advanced and addressing a specific pain point in computational pathology (unbalanced MoE routing), it currently exists as a zero-star research repository, making its defensibility primarily tied to the complexity of the domain rather than ecosystem lock-in.

COMPOSABILITY

TECH STACK

pythonpytorchoptimal_transportmultiple_instance_learninggraph_neural_networks

INTEGRATION

reference_implementation

wsi_classificationmultiple_instance_learningmixture_of_expertsoptimal_transportcomputational_pathology

READINESS

Composabilityalgorithm

Depthreference_implementation