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AI Research
arXiv CS.AI·Jun 9·5 min read
Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
TL;DR
Temporomandibular joint osteoarthritis (TMJ OA) is difficult to detect using cone-beam CT (CBCT) due to subtle changes. A pipeline using self-supervised Vision Transformers, specifically DINOv2 with partial unfreezing, was developed for patient-level classification of OA versus normal.
✦ Why It Matters
Engineers can leverage these findings to improve automated detection systems in medical imaging with limited data.
Key Takeaways
- Explore self-supervised models for medical imaging tasks.
- Utilize partial unfreezing of transformer blocks for better performance.
- Implement attention-based multiple instance learning for classification.
- Focus on adaptation strategies over model choice in low-data scenarios.
How It Works
The study employs a slice-based pipeline where each axial CBCT slice is encoded using a Vision Transformer (ViT). The model uses attention-based multiple instance learning (MIL) to aggregate information across slices for patient-level classification, allowing it to effectively identify subtle changes indicative of TMJ OA.