做工业无损检测或AI可解释性研究的团队会感兴趣——p-ResNet-50在保持高精度的同时让黑盒模型变得可审计，航空质检场景可以直接参考其原型对齐方法。
航空SiC/SiC复合材料的X射线CT无损检测依赖专家目视评估，缺乏可追溯性。研究团队提出p-ResNet-50，在卷积网络中引入原型层，将高检测精度与基于案例的解释结合。六个学习原型与专家定义的语义类别（健康基体、基体-空气界面、孔隙、线状缺陷、混合形态）对齐，每个分类都可追溯到物理有意义的参考。通过锚点和中心点正则化项防止原型坍缩，UMAP潜空间分析明确标出模型可靠与不可靠区域。在约12000个补丁的数据集上，p-ResNet-50达到与黑盒ResNet-50相当的精度（0.957 vs 0.959），同时提供可追溯决策和不确定性标记。
Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
Non-destructive testing of aerospace SiC/SiC composites via X-ray computed tomography (XCT) relies on expert visual assessment, with current workflows offering limited traceability for accept/reject decisions. Deep convolutional networks can automate defect detection, yet their black-box nature conflicts with the transparency that industrial inspection practice demands. To close this gap, we introduce p-ResNet-50, a convolutional framework extended with a prototype layer that couples high detection accuracy with case-based explanations. Six learned prototypes are explicitly aligned with expert-defined semantic categories-healthy matrix, matrix--air interfaces, pores, line-like defects, and mixed morphologies-so that every classification is traceable to a physically meaningful reference. Two novel regularisation terms, anchor-based and medoid-based, tether prototypes to expert-selected patches and prevent prototype collapse, addressing a known limitation of prototype networks. Latent-space analysis via UMAP delineates semantically coherent sub-domains and maps zones of uncertainty where misclassifications concentrate, giving inspectors an explicit picture of where the model is-and is not-reliable. The framework is validated on an XCT patch dataset of approximately 12,000 patches extracted from four defect-rich SiC/SiC laboratory specimens. Taking a black-box ResNet-50 as a baseline (ROC-AUC = 0.991), the prototype extension achieves comparable performance (accuracy 0.957 vs. 0.959; ROC-AUC 0.994 vs. 0.993) while trading a slight reduction in sensitivity for higher precision and specificity. Each decision is backed by representative evidence patches, and the model explicitly flags its uncertainty regions. Beyond defect mapping, the framework establishes a reusable methodology for embedding domain-expert knowledge into prototype networks, applicable to other XCT inspection scenarios requiring traceable, auditable decisions.