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DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis

Kun Tang, Xinquan Yang, Mianjie Zheng, Xuefen Liu, Xuguang Li, Xiaoqi Guo, Ruihan Chen, Linlin Shen, He Meng

Abstract

The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervised vision foundation model pre-trained on 1.7 billion images, offers a promising pathway to mitigate this issue. However, its reliability when transferred to the dental domain, with its unique imaging characteristics and clinical subtleties, remains unclear. To address this, we introduce DinoDental, a unified benchmark designed to systematically evaluate whether DINOv3 can serve as a reliable, off-the-shelf encoder for comprehensive dental image analysis without requiring domain-specific pre-training. Constructed from multiple public datasets, DinoDental covers a wide range of tasks, including classification, detection, and instance segmentation on both panoramic radiographs and intraoral photographs. We further analyze the model's transfer performance by scaling its size and input resolution, and by comparing different adaptation strategies, including frozen features, full fine-tuning, and the parameter-efficient Low-Rank Adaptation (LoRA) method. Our experiments show that DINOv3 can serve as a strong unified encoder for dental image analysis across both panoramic radiographs and intraoral photographs, remaining competitive across tasks while showing particularly clear advantages for intraoral image understanding and boundary-sensitive dense prediction. Collectively, DinoDental provides a systematic framework for comprehensively evaluating DINOv3 in dental analysis, establishing a foundational benchmark to guide efficient and effective model selection and adaptation for the dental AI community.

DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis

Abstract

The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervised vision foundation model pre-trained on 1.7 billion images, offers a promising pathway to mitigate this issue. However, its reliability when transferred to the dental domain, with its unique imaging characteristics and clinical subtleties, remains unclear. To address this, we introduce DinoDental, a unified benchmark designed to systematically evaluate whether DINOv3 can serve as a reliable, off-the-shelf encoder for comprehensive dental image analysis without requiring domain-specific pre-training. Constructed from multiple public datasets, DinoDental covers a wide range of tasks, including classification, detection, and instance segmentation on both panoramic radiographs and intraoral photographs. We further analyze the model's transfer performance by scaling its size and input resolution, and by comparing different adaptation strategies, including frozen features, full fine-tuning, and the parameter-efficient Low-Rank Adaptation (LoRA) method. Our experiments show that DINOv3 can serve as a strong unified encoder for dental image analysis across both panoramic radiographs and intraoral photographs, remaining competitive across tasks while showing particularly clear advantages for intraoral image understanding and boundary-sensitive dense prediction. Collectively, DinoDental provides a systematic framework for comprehensively evaluating DINOv3 in dental analysis, establishing a foundational benchmark to guide efficient and effective model selection and adaptation for the dental AI community.

Paper Structure

This paper contains 28 sections, 9 equations, 6 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Self-supervised visual foundation models
  4. Vision--language pretraining for medical image analysis
  5. DINO family
  6. Benchmark Setup
  7. Datasets
  8. Object Detection and Instance Segmentation
  9. Semantic Segmentation
  10. Classification
  11. Metrics
  12. Finetuning Strategies
  13. Task Setup
  14. Experiments
  15. Task-modality Adaptability
  16. ...and 13 more sections

Figures (6)

  • Figure 1: Qualitative detection comparisons on the DENTEX dataset.
  • Figure 2: Qualitative detection comparisons on the AlphaDent dataset.
  • Figure 3: Qualitative segmentation comparison on the Multi-center Panoramic dataset. Red masks indicate impacted-tooth regions.
  • Figure 4: Confusion matrices of the multi-label classification on OralXrays-9 dataset.
  • Figure 5: Visualization of the different resolution sensitivity of different DINOv3 sizes on panoramic radiographs.
  • ...and 1 more figures