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.
V oco: A simple-yet-effective volume contrastive learning 11 framework for 3d medical image analysis
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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.