MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis
This addresses the challenge of improving diagnostic accuracy in medical AI by enabling more personalized pathological relationship modeling, though it appears incremental as it builds on existing GNN frameworks.
The paper tackled the problem of graph neural networks relying on a single static graph for multimodal medical diagnosis, which hinders patient-specific modeling, and proposed MAPI-GNN to learn multifaceted graph profiles, resulting in significant outperformance over state-of-the-art methods on over 1300 patient samples.
Graph neural networks are increasingly applied to multimodal medical diagnosis for their inherent relational modeling capabilities. However, their efficacy is often compromised by the prevailing reliance on a single, static graph built from indiscriminate features, hindering the ability to model patient-specific pathological relationships. To this end, the proposed Multi-Activation Plane Interaction Graph Neural Network (MAPI-GNN) reconstructs this single-graph paradigm by learning a multifaceted graph profile from semantically disentangled feature subspaces. The framework first uncovers latent graph-aware patterns via a multi-dimensional discriminator; these patterns then guide the dynamic construction of a stack of activation graphs; and this multifaceted profile is finally aggregated and contextualized by a relational fusion engine for a robust diagnosis. Extensive experiments on two diverse tasks, comprising over 1300 patient samples, demonstrate that MAPI-GNN significantly outperforms state-of-the-art methods.