mlsanityData to 14 Oct 2026

Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging

arXiv 2407.21381 ↗First posted 31 Jul 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

Knee osteoarthritis (KOA), a common form of arthritis that causes physical disability, has become increasingly prevalent in society. Employing computer-aided techniques to automatically assess the severity and progression of KOA can greatly benefit KOA treatment and disease management. Particularly, the advancement of X-ray technology in KOA demonstrates its potential for this purpose. Yet, existing X-ray prognosis research generally yields a singular progression severity grade, overlooking the potential visual changes for understanding and explaining the progression outcome. Therefore, in this study, a novel generative model is proposed, namely Identity-Consistent Radiographic Diffusion Network (IC-RDN), for multifaceted KOA prognosis encompassing a predicted future knee X-ray scan conditioned on the baseline scan. Specifically, an identity prior module for the diffusion and a downstream generation-guided progression prediction module are introduced. Compared to conventional image-to-image generative models, identity priors regularize and guide the diffusion to focus more on the clinical nuances of the prognosis based on a contrastive learning strategy. The progression prediction module utilizes both forecasted and baseline knee scans, and a more comprehensive formulation of KOA severity progression grading is expected. Extensive experiments on a widely used public dataset, OAI, demonstrate the effectiveness of the proposed method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 31 Jul 2024.

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18 of 47 references.

References · 18

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  8. 2019Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Datacited by 23
  9. 2019Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networkscited by 18
  10. 2016Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networkscited by 17
  11. 2017Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networkscited by 16
  12. 2020Deep learning predicts total knee replacement from magnetic resonance imagescited by 10
6 more
  1. 2020Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographscited by 8
  2. 2019Assessing Knee OA Severity with CNN attention-based end-to-end architecturescited by 6
  3. 2021Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Networkcited by 6
  4. 2019Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal datacited by 3
  5. 2023Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritiscited by 3
  6. 2019Towards Shape-based Knee Osteoarthritis Classification using Graph Convolutional Networkscited by 2

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