ProtoPointNet：基于原型的具有可验证空间激活的3D牙齿点云可解释分类
ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations
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中文总结 AI 辅助
研究针对3D牙齿点云分类，提出ProtoPointNet模型，结合局部表面几何等特征编码点，通过共享主干学习特定轴原型头，用辅助监督等训练，在Bits2Bites上取得较好结果，其激活定位合理，为牙齿表面对分析提供新的可解释方法。
中文摘要 AI 辅助
基于原型的网络通过将预测与学习到的范例联系起来提供内在可解释的分类，但它们在3D点云和临床表面对推理中的应用仍然有限。我们引入了ProtoPointNet，这是一种基于原型的模型，用于对注册的上下颌牙弓对进行牙合分类。每个点由一个14维描述符编码，该描述符结合了局部表面几何形状、曲率以及明确的牙弓间位移和间隙，将咬合关系暴露于原型匹配。一个共享的多任务点云主干为矢状左、矢状右、垂直、横向和中线分类学习特定轴的原型头。为了支持有限的临床数据，我们使用辅助监督和无编码器冻结交接从头开始训练原型。在Bits2Bites上，ProtoPointNet实现了平均测试宏F1为0.724和AUROC为0.825，在垂直分类（F1 0.828）和矢状左分类（F1 0.807）上表现最强。投影的原型激活定位到解剖学上合理的区域，包括用于反牙合证据的后磨牙和前磨牙以及用于咬合深度证据的前牙。这些结果支持基于原型的推理作为用于牙齿表面对分析的黑盒3D分类器的透明、基于空间的替代方案。
英文摘要
Prototype-based networks provide inherently interpretable classification by linking predictions to learned exemplars, but their use in 3D point clouds and clinical surface-pair reasoning remains limited. We introduce ProtoPointNet, a prototype-based model for dental occlusion classification from registered upper--lower intraoral arch pairs. Each point is encoded by a 14-dimensional descriptor combining local surface geometry, curvature, and explicit inter-arch displacement and clearance, exposing occlusal relationships to prototype matching. A shared multi-task point-cloud backbone learns axis-specific prototype heads for sagittal-left, sagittal-right, vertical, transverse, and midline classification. To support limited clinical data, we train prototypes from scratch using auxiliary supervision and encoder-freeze hand-off. On Bits2Bites, ProtoPointNet achieves mean test macro-F1 of 0.724 and AUROC of 0.825, with strongest performance on vertical (F1 0.828) and sagittal-left classification (F1 0.807). Projected prototype activations localise to anatomically plausible regions, including posterior molars and premolars for cross-bite evidence and anterior incisors for bite-depth evidence. These results support prototype-based reasoning as a transparent, spatially grounded alternative to black-box 3D classifiers for dental surface-pair analysis.
发表机构
- Adelaide Dental School, Adelaide University（阿德莱德牙科学院，阿德莱德大学）
- Australian Institute for Machine Learning, Adelaide University（澳大利亚机器学习研究所，阿德莱德大学）
- School of Public Health, Adelaide University（阿德莱德大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。
