用多模态与对称损失提升膝骨关节炎诊断一致性
CLIP-KOA: Enhancing Knee Osteoarthritis Diagnosis with Multi-Modal Learning and Symmetry-Aware Loss Functions
- 基于CLIP融合影像与文本信息，提升诊断理解力
- 在KOA分级任务中达71.86%准确率，较标准CLIP提升2.36%
- 适合医疗图像分析与细粒度疾病诊断研究者参考
膝骨关节炎（KOA）是全球普遍的慢性骨骼肌肉疾病，早期诊断至关重要。目前广泛使用的凯尔格伦-劳伦斯（KL）分级系统存在高观察者间差异和主观性，影响诊断一致性。近年来，深度学习驱动的自动化诊断方法受到关注。本文提出基于CLIP的CLIP-KOA框架，通过融合图像与文本信息，并引入对称性损失与一致性损失，确保原图与翻转图预测一致。该方法在KOA严重程度预测任务中达到71.86%的准确率，相较标准CLIP模型提升2.36%。实验表明，该方法为数据驱动的医学诊断提供了新方向，不仅提升了细粒度诊断的可靠性，也拓展了多模态医学图像分析的应用边界。代码已公开于https://github.com/anonymized-link。
原文摘要 · Abstract (English)
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.
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