manwaarkhd/aariz
A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification
Comprises 1,000 lateral cephalometric radiographs from 7 diverse imaging devices with expert annotations of 29 anatomical landmarks and Cervical Vertebral Maturation (CVM) stages—the first unified resource for both landmark detection and CVM classification tasks. Provides stratified train/validation/test splits across all imaging devices to ensure fair evaluation and device-agnostic model generalization. Includes a Python `AarizDataset` class for direct integration with ML pipelines, alongside published baseline results (MRE 1.69±3.36mm) for benchmarking detection accuracy at clinically relevant thresholds.
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Python
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MIT
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Aug 06, 2025
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