Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis
Cone Beam Computed Tomography (CBCT) is pivotal for 3D diagnostic imaging in dentistry. However, the development of robust AI models for volumetric analysis is often constrained by the scarcity of large, annotated datasets. Self-supervised learning (SSL), particularly Masked Image Modeling (MIM), offers a promising pathway to leverage unlabeled data. A limitation of standard MIM is its reliance on random masking, which fails to prioritize diagnostically critical regions in dental CBCT volumes, such as subtle pathological changes and intricate anatomical boundaries. To address this, we propose ATMask, a novel adaptive masking strategy. Instead of applying random masks or employing computat...
| Authors | Xinquan Yang, Jianfeng Ren, Xuguang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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