Hypergraph Neural Networks with Attention-based Fusion for Multimodal Medical Data Integration and Analysis
Abhishek Kumar, Abhijieet Nashte, Ron Amit, Chahil Choudhary
Abstract
In an exploration into the intricate domain of multimodal medical data synthesis, this investigation underscores the transformative potential of avant-garde neural architectures in the medical diagnostics landscape. Central to this endeavor is the pioneering amalgamation of HCNN-MAFN techniques, crafted to harness the prowess of Hypergraph Neural Networks in tandem with attention-centric fusion. When executed on the ADNI dataset, a profound reservoir of Alzheimer’s Disease insights, the algorithm unveiled marked enhancements in data deciphering and elucidation. The essence of this research resonates with its capability to reshape medical data analytics, seamlessly navigating the nexus between complex data modalities and decipherable clinical revelations. Preliminary results indicate a notable enhancement in performance metrics over conventional methods, underscoring the algorithm’s efficacy. In sum, the research introduces a robust method for the fusion and analysis of complex medical datasets, presenting a promising avenue for future diagnostic and therapeutic applications.