# Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis

arXiv: https://arxiv.org/abs/2605.01741 · HTML: https://mlsanity.com/paper/2605.01741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 20 of 20)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [What to Hide from Your Students: Attention-Guided Masked Image Modeling](https://mlsanity.com/paper/2203.12719/) (2022)
- [VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis](https://mlsanity.com/paper/2402.17300/) (2024)
- [Masked Image Modeling Advances 3D Medical Image Analysis](https://mlsanity.com/paper/2204.11716/) (2022)
- [Hard Patches Mining for Masked Image Modeling](https://mlsanity.com/paper/2304.05919/) (2023)
- [SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12591/) (2023)
- [Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder](https://mlsanity.com/paper/2306.08913/) (2023)
- [HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation](https://mlsanity.com/paper/2303.10333/) (2023)
- [CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images](https://mlsanity.com/paper/2206.08778/) (2022)
- [Two-Stream Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2305.10044/) (2023)
- [TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2306.14406/) (2023)
- [Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning](https://mlsanity.com/paper/2507.02581/) (2025)
- [MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration](https://mlsanity.com/paper/2512.02867/) (2025)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)

## Abstract (arXiv)

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 computationally intensive attention modules, ATMask computes an inter-slice texture variation map to identify regions with high structural or textural complexity. These high-variation areas are then selectively masked during pre-training, compelling the model to learn richer contextual representations essential for inferring complex 3D morphological transitions. Furthermore, we contribute the first large-scale CBCT dataset, curated from both public and private sources, comprising 6,314 scans, for the dental AI model pretraining. Extensive experiments on three downstream dental CBCT tasks demonstrate that our ATMask enables more data-efficient and powerful representation learning than standard random masking and other advanced SSL baselines. The dataset and code will be released.