arXiv Computer Vision
Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
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Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed tomography (CBCT) is used for visual measurement,...
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ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations
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.
arXiv Computer Vision
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Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification
arXiv:2609.14703v1 Announce Type: new Abstract: Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their d...
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Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification
Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their detection, treatment planning, and follow-up. Auto...
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LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata
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arXiv Machine Learning
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Detection is solved, delineation is not: what governs tooth segmentation on panoramic radiographs
The study evaluates automatic tooth segmentation on panoramic radiographs using a large annotated corpus of 1,422 images and 42,142 tooth polygons. It finds that increasing input resolution improves boundary precision (mask mAP50‑95 rises from 0.656 to 0.717) while detection performance remains unchanged, and that architectural changes have minimal impact on in‑domain accuracy. Targeted interventions such as LoRA adaptation, promptable foundation models, and anatomical label assignment provide negligible gains, indicating that resolution and acquisition diversity should be prioritized over model novelty.