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CLIP-KOA: Enhancing Knee Osteoarthritis Diagnosis with Multi-Modal Learning and Symmetry-Aware Loss Functions

Yejin Jeong, Donghun Lee

TL;DR

KOA diagnosis suffers from inter-observer variability in KL grading. The paper introduces CLIP-KOA, a multimodal CLIP-based framework that fuses knee X-ray images with textual grade descriptions, augmented by symmetry-aware losses to enforce consistent predictions under horizontal flips. It achieves state-of-the-art KOA severity accuracy of 71.86%, with a 2.36% gain over a standard CLIP baseline, and ablations confirm the benefit of Symmetry Loss and Consistency Loss, especially for intermediate grades. This work demonstrates the value of integrating textual descriptors and symmetry-regularized multimodal learning for more reliable, fine-grained KOA grading, and points to directions like richer textual inputs and adaptive loss weighting.

Abstract

Knee osteoarthritis (KOA) is a universal chronic musculoskeletal disorders worldwide, making early diagnosis crucial. Currently, the Kellgren and Lawrence (KL) grading system is widely used to assess KOA severity. However, its high inter-observer variability and subjectivity hinder diagnostic consistency. To address these limitations, automated diagnostic techniques using deep learning have been actively explored in recent years. In this study, we propose a CLIP-based framework (CLIP-KOA) to enhance the consistency and reliability of KOA grade prediction. To achieve this, we introduce a learning approach that integrates image and text information and incorporate Symmetry Loss and Consistency Loss to ensure prediction consistency between the original and flipped images. CLIP-KOA achieves state-of-the-art accuracy of 71.86\% on KOA severity prediction task, and ablation studies show that CLIP-KOA has 2.36\% improvement in accuracy over the standard CLIP model due to our contribution. This study shows a novel direction for data-driven medical prediction not only to improve reliability of fine-grained diagnosis and but also to explore multimodal methods for medical image analysis. Our code is available at https://github.com/anonymized-link.

CLIP-KOA: Enhancing Knee Osteoarthritis Diagnosis with Multi-Modal Learning and Symmetry-Aware Loss Functions

TL;DR

KOA diagnosis suffers from inter-observer variability in KL grading. The paper introduces CLIP-KOA, a multimodal CLIP-based framework that fuses knee X-ray images with textual grade descriptions, augmented by symmetry-aware losses to enforce consistent predictions under horizontal flips. It achieves state-of-the-art KOA severity accuracy of 71.86%, with a 2.36% gain over a standard CLIP baseline, and ablations confirm the benefit of Symmetry Loss and Consistency Loss, especially for intermediate grades. This work demonstrates the value of integrating textual descriptors and symmetry-regularized multimodal learning for more reliable, fine-grained KOA grading, and points to directions like richer textual inputs and adaptive loss weighting.

Abstract

Knee osteoarthritis (KOA) is a universal chronic musculoskeletal disorders worldwide, making early diagnosis crucial. Currently, the Kellgren and Lawrence (KL) grading system is widely used to assess KOA severity. However, its high inter-observer variability and subjectivity hinder diagnostic consistency. To address these limitations, automated diagnostic techniques using deep learning have been actively explored in recent years. In this study, we propose a CLIP-based framework (CLIP-KOA) to enhance the consistency and reliability of KOA grade prediction. To achieve this, we introduce a learning approach that integrates image and text information and incorporate Symmetry Loss and Consistency Loss to ensure prediction consistency between the original and flipped images. CLIP-KOA achieves state-of-the-art accuracy of 71.86\% on KOA severity prediction task, and ablation studies show that CLIP-KOA has 2.36\% improvement in accuracy over the standard CLIP model due to our contribution. This study shows a novel direction for data-driven medical prediction not only to improve reliability of fine-grained diagnosis and but also to explore multimodal methods for medical image analysis. Our code is available at https://github.com/anonymized-link.
Paper Structure (12 sections, 4 equations, 2 figures, 3 tables)

This paper contains 12 sections, 4 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Problem formulation
  4. Loss function
  5. Symmetry Loss
  6. Consistency Loss
  7. Experiments
  8. Dataset
  9. Implementation Details
  10. Results
  11. Ablation Study
  12. Conclusion

Figures (2)

  • Figure 1: Symmetry Loss and Consistency Loss framework for knee osteoarthritis (KOA) severity grading. The original image $I$ and its horizontally flipped version $I^{H}$ are mapped to similarity matrices $\mathcal{S}$ and $\mathcal{S}^{H}$ through the image and text encoders. Symmetry Loss is computed using the cross-entropy loss between the similarity matrices and one-hot encoded labels. Jensen-Shannon Divergence (JSD) is used in $\mathcal{L}_{consistency}$ to measure the difference between the distributions of $\mathcal{S}$ and $\mathcal{S}^{H}$. The final loss function integrates Symmetry Loss and Consistency Loss to enhance the robustness of KOA severity classification.
  • Figure 2: The figure presents a comparison of confusion matrices for KL grade prediction between the baseline CLIP model (left) and the proposed CLIP-KOA model (right). X-axis (Predicted Label): The five KL grades predicted by the model: Normal, Doubtful, Minimal, Moderate, and Severe. Y-axis (True Label): The actual KL grades assigned in the dataset.