用低倍电镜图像就能自动判断陶瓷假体断裂原因，提升质检效率。
Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina
- 基于可解释的视觉变压器，自动分类陶瓷假体三种制造缺陷
- 50倍低倍率下准确率达90.7%，与高倍率效果相当
- 结果可视化对齐传统断口分析标准，适合医疗质量管控场景
可靠识别氧化铝基复合材料髋膝关节假体的断裂源对质量保证和患者安全至关重要，但现有断口分析流程耗时、主观性强，且依赖高倍扫描电镜（SEM）。我们提出一种可解释的视觉变压器（ViT）工作流，用于自动化分类广泛应用于人工关节置换的氧化铝基复合材料（BIOLOX delta, CeramTec GmbH）的断裂成因。数据集包含8,493张来自五年生产中爆破和抽检测试的SEM图像（放大倍数50x–10,000x），按制造链划分为三类缺陷：生坯、硬加工和材料缺陷。在严重类别不平衡条件下，微调后的ViT在分层五折交叉验证中达到0.907的准确率和0.888的宏平均F1值，通过两阶段感知哈希/SSIM泄漏审计确认样本无重叠。值得注意的是，50倍低倍率下的性能与1,000x–10,000x高倍率相当，表明宏观特征——镜面区几何形状与解理线场——已蕴含足够诊断信息。Grad-CAM注意力图始终聚焦于经典断口特征（镜面区、解理线、孔隙、加工痕迹），符合既定断口分析标准。综合结果表明，可解释的ViT可作为陶瓷假体质量控制的互补工具，实现低倍预筛，减少对耗时高倍检测的依赖。
原文摘要 · Abstract (English)
Reliable identification of fracture origins in alumina matrix composite hip and knee implants is critical for quality assurance and patient safety, yet current fractographic workflows are time-consuming, partly subjective, and reliant on high-magnification scanning electron microscopy (SEM). We present an interpretable vision-transformer (ViT) workflow for automated classification of fracture causes in an alumina matrix composite (BIOLOX delta, CeramTec GmbH) widely used in total joint replacements. A dataset of 8,493 SEM images (50x-10,000x) was curated from five years of in-production burst and proof tests and annotated into three defect categories defined along the manufacturing chain: green body, hard machining, and material defects. Under severe class imbalance, the fine-tuned ViT reached an accuracy of 0.907 and a macro-F1 of 0.888 in stratified five-fold cross-validation, with a two-stage perceptual-hash/SSIM leakage audit confirming negligible specimen overlap. Notably, performance at low magnification (50x) was comparable to that at high magnification (1k-10kx), indicating that macro-scale features - mirror geometry and hackle line fields - already encode sufficient diagnostic signal. Grad-CAM attributions consistently localised on canonical fractographic cues (mirrors, hackles, pores, machining marks), aligning with established fractographic criteria. Together, these results position interpretable ViTs as a complementary tool for ceramic-implant quality assurance, enabling low-magnification pre-screening and reducing reliance on time-intensive high-magnification inspection.
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