GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis
By: Xuhao Shan , Ruiquan Ge , Jikui Liu and more
Potential Business Impact:
Helps doctors predict sickness better using different patient info.
In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenges lie in effectively modeling the complex interactions between heterogeneous data modalities with distinct characteristics while capturing both local and global dependencies across modalities. To address these challenges, this paper presents a two-stage multimodal prognosis model, GraphMMP, which is based on graph neural networks. The proposed model constructs feature graphs using mutual information and features a global fusion module built on Mamba, which significantly boosts prognosis performance. Empirical results show that GraphMMP surpasses existing methods on datasets related to liver prognosis and the METABRIC study, demonstrating its effectiveness in multimodal medical prognosis tasks.
Similar Papers
MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis
CV and Pattern Recognition
Helps doctors diagnose sickness better using patient data.
ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Image and Video Processing
Makes medical scans clearer for better diagnoses.
Unsupervised Multimodal Graph-based Model for Geo-social Analysis
Social and Information Networks
Finds important news in social media posts.