Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
Researchers have developed a new framework for medical image classification that integrates multimodal knowledge graphs and a reliability-guided refinement process. This approach aims to mimic clinical diagnosis by leveraging historical similar cases and external knowledge, moving beyond isolated visual evidence. The system constructs knowledge graphs from retrieved cases, uses graph attention networks for knowledge propagation, and employs cross-modal attention for alignment, ultimately refining predictions based on case reliability. AI
IMPACT This research introduces a novel approach to medical image classification by incorporating case-based reasoning and knowledge graphs, potentially leading to more explainable and accurate diagnoses.