mlsanityData to 14 Oct 2026

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

arXiv 2403.06498 ↗First posted 11 Mar 2024eess.IV cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 9 of its 24 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

9 of 24 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2016Wide Residual Networkscited by 3,070
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  7. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  8. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  9. 2021ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classificationcited by 35

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
  2. 2023Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
  3. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  4. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  5. 2024BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
  6. 2023Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse
  7. 2024DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training
  8. 2024Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images