🤖 AI Summary
This work addresses the challenges of interpretability and precise lesion localization in chest X-ray diagnosis by proposing a graph neural network framework that integrates anatomical structure priors. The method reformulates convolutional feature maps into patch-level graphs incorporating both appearance features and spatial coordinates, and introduces a tailored structural propagation mechanism that explicitly models relative anatomical relationships among nodes, thereby endowing the graph network with an inductive bias for structured reasoning. Intrinsic interpretability is achieved through node importance scoring and joint graph-node prediction, eliminating the need for post-hoc visualization. Experimental results demonstrate that the proposed approach effectively enhances both diagnostic accuracy and model interpretability, while exhibiting strong potential for cross-domain generalization.
📝 Abstract
We present a structural graph reasoning framework that incorporates explicit anatomical priors for explainable vision-based diagnosis. Convolutional feature maps are reinterpreted as patch-level graphs, where nodes encode both appearance and spatial coordinates, and edges reflect local structural adjacency. Unlike conventional graph neural networks that rely on generic message passing, we introduce a custom structural propagation mechanism that explicitly models relative spatial relations as part of the reasoning process. This design enables the graph to act as an inductive bias for structured inference rather than a passive relational representation. The proposed model jointly supports node-level lesion-aware predictions and graph-level diagnostic reasoning, yielding intrinsic explainability through learned node importance scores without relying on post-hoc visualization techniques. We demonstrate the approach through a chest X-ray case study, illustrating how structural priors guide relational reasoning and improve interpretability. While evaluated in a medical imaging context, the framework is domain-agnostic and aligns with the broader vision of graph-based reasoning across artificial intelligence systems. This work contributes to the growing body of research exploring graphs as computational substrates for structure-aware and explainable learning.