The content discusses the challenges of traditional osteoporosis diagnosis methods and introduces a novel semi-supervised model based on diffusion models and sinusoidal threshold decay. This approach utilizes synthetic data generated by the diffusion model to improve performance compared to real unlabeled data. The method is tested on dental panoramic images, achieving leading detection performance with 80.10% accuracy. The study highlights the importance of computer-aided diagnosis in improving patient outcomes and quality of life by enabling timely treatment and reducing fracture risks associated with osteoporosis.
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