Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method
  and Diffusion Models for Osteoporosis Diagnosis
Wenchi Ke
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.