ATMask adaptively masks high inter-slice texture variation regions in 3D CBCT volumes during self-supervised pretraining, enabling more data-efficient learning than random masking on dental tasks with a contributed 6314-scan dataset.
Exploring chatgpt’s potential in diag - nosing oral and maxillofacial pathologies: a study of 123 challenging cases
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Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis
ATMask adaptively masks high inter-slice texture variation regions in 3D CBCT volumes during self-supervised pretraining, enabling more data-efficient learning than random masking on dental tasks with a contributed 6314-scan dataset.