将骨骼结构图融入影像模型，提升骨关节炎诊断准确率
Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models
- 用SAM分割构建骨骼结构图，与影像特征联合建模
- 在OAI数据集上准确率达79.8%，比基线高10%
- 适合医学影像分析、精准诊疗研究者参考
基于放射影像的膝骨关节炎（KOA）诊断仍面临挑战，因标准深度学习模型难以有效捕捉细微形态学特征。本文提出一种新型多模态框架，通过将从分割任意模型（SAM）分割结果生成的形态学图表示与视觉编码器结合，实现几何信息与影像特征对齐。利用互信息最大化约束图嵌入与影像特征的一致性，显著提升分类性能。通过构建反映解剖结构的图，引入符合临床评估标准的先验知识，丰富特征空间并增强模型归纳偏置。在骨关节炎倡议（Osteoarthritis Initiative, OAI）数据集上的实验表明，该方法相比单模态基线最高提升10%准确率（达79.8%），优于现有最先进方法8%准确率和11% F1分数，验证了将解剖结构信息融入放射影像分析对准确评估KOA严重程度的关键作用。
原文摘要 · Abstract (English)
Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively. We propose a novel multimodal framework that combines anatomical structure with radiographic features by integrating a morphological graph representation - derived from Segment Anything Model (SAM) segmentations - with a vision encoder. Our approach enforces alignment between geometry-informed graph embeddings and radiographic features through mutual information maximization, significantly improving KOA classification accuracy. By constructing graphs from anatomical features, we introduce explicit morphological priors that mirror clinical assessment criteria, enriching the feature space and enhancing the model's inductive bias. Experiments on the Osteoarthritis Initiative dataset demonstrate that our approach surpasses single-modality baselines by up to 10\% in accuracy (reaching nearly 80\%), while outperforming existing state-of-the-art methods by 8\% in accuracy and 11\% in F1 score. These results underscore the critical importance of incorporating anatomical structure into radiographic analysis for accurate KOA severity grading.
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