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Self-supervised vision transformers show promise for TMJ OA detection

By PulseAugur Editorial · [2 sources] ·

Researchers have explored the effectiveness of self-supervised vision transformers, specifically the DINO family, for detecting temporomandibular joint osteoarthritis (TMJ OA) from cone-beam CT (CBCT) scans. Their study found that partially unfreezing the final two transformer blocks significantly improved the Area Under the Curve (AUC) for classification from 0.671 to 0.902. This adaptation strategy proved more critical than the choice of backbone model itself, offering practical insights for applying these models in low-data medical imaging scenarios. AI

IMPACT Demonstrates a novel approach for adapting foundation models to medical imaging, potentially improving diagnostic accuracy in low-data settings.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Self-supervised vision transformers show promise for TMJ OA detection

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Shradhdha Trivedi, Vrundan Sojitra, Mariela Padilla ·

    Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis

    arXiv:2606.08364v1 Announce Type: cross Abstract: Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging. We study how well the DINO family of self…

  2. arXiv cs.AI TIER_1 English(EN) · Mariela Padilla ·

    Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis

    Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging. We study how well the DINO family of self-supervised vision transformers -- DINOv1, DINOv2,…

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