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

Wenchi Ke

TL;DR

A novel pseudo-label threshold adjustment mechanism, Sinusoidal Threshold Decay, which can make the semi-supervised model converge more quickly and improve its performance and its achieved leading detect performance and produces a 80.10% accuracy.

Abstract

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.

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

TL;DR

A novel pseudo-label threshold adjustment mechanism, Sinusoidal Threshold Decay, which can make the semi-supervised model converge more quickly and improve its performance and its achieved leading detect performance and produces a 80.10% accuracy.

Abstract

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.
Paper Structure (14 sections, 1 equation, 3 figures, 3 tables)

This paper contains 14 sections, 1 equation, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Relate work
  3. Method
  4. Materials
  5. Architecture
  6. Generating Synthetic Data Based on Denoising Diffusion Probabilistic Models(DDPM)
  7. Semi-supervised methods based on simulated data.
  8. Sinusoidal Threshold Decay for Consistency Learning
  9. Experiments and Results
  10. Evaluation Metrics and Implementation.
  11. compared with other methods
  12. Simulation data or real data
  13. Effectiveness of Sinusoidal Threshold Decay
  14. Conclusion

Figures (3)

  • Figure 1: The overall Architecture of the method in this paper.
  • Figure 2: Results of synthetic data generated by different models. From left to right: a) Denoising Diffusion Probabilistic Model(DDPM); b) real data ;c)Generative Adversarial model(GAN).
  • Figure 3: Visualized threshold changes.