Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method and Diffusion Models for Osteoporosis Diagnosis
This work addresses the problem of expensive and complex traditional osteoporosis diagnosis methods for patients, offering a more convenient and accurate automated approach, though it appears incremental in its improvements to semi-supervised techniques.
The paper tackled osteoporosis diagnosis by proposing a semi-supervised model using diffusion-generated synthetic data and a sinusoidal threshold decay mechanism, achieving 80.10% accuracy on a dataset of 749 dental panoramic images.
Osteoporosis is a common skeletal disease that seriously affects patients' quality of life. Traditional osteoporosis diagnosis methods are expensive and complex. The semi-supervised model based on diffusion model and class threshold sinusoidal decay proposed in this paper can automatically diagnose osteoporosis based on patient's imaging data, which has the advantages of convenience, accuracy, and low cost. Unlike previous semi-supervised models, all the unlabeled data used in this paper are generated by the diffusion model. Compared with real unlabeled data, synthetic data generated by the diffusion model show better performance. In addition, this paper proposes a novel pseudo-label threshold adjustment mechanism, Sinusoidal Threshold Decay, which can make the semi-supervised model converge more quickly and improve its performance. Specifically, the method is tested on a dataset including 749 dental panoramic images, and its achieved leading detect performance and produces a 80.10% accuracy.