具有解剖先验的结构图神经网络用于可解释的胸部X光诊断
Structural Graph Neural Networks with Anatomical Priors for Explainable Chest X-ray Diagnosis
- Berkani Khaled（伯克尼卡德）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合解剖先验的结构图神经网络，用于可解释的胸部X光诊断，通过结构传播机制提升模型的可解释性和诊断准确性。
AI中文摘要：
我们提出了一种结构图推理框架，该框架结合了显式的解剖先验，用于可解释的基于视觉的诊断。卷积特征图被重新解释为块级图，其中节点编码了外观和空间坐标，边反映了局部结构邻接。与传统图神经网络依赖通用消息传递不同，我们引入了一种定制的结构传播机制，该机制在推理过程中显式地建模相对空间关系。这种设计使图能够作为结构推断的归纳偏差，而不是被动的关系表示。所提出的模型联合支持节点级病变感知预测和图级诊断推理，通过学习的节点重要性评分实现内在的可解释性，而无需依赖事后可视化技术。我们通过胸部X光案例研究展示了该方法，说明了结构先验如何引导关系推理并提高可解释性。尽管在医学成像背景下进行评估，该框架是领域无关的，并与人工智能系统中基于图的推理的更广泛愿景一致。这项工作为探索图作为结构感知和可解释学习计算子基质的研究做出了贡献。
英文摘要：
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.
