基于MRI的深度学习模型在全膝关节置换预测中的泛化能力改进
Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction
AI总结：
针对MRI深度学习模型在不同来源数据上泛化性差的问题，提出用实例归一化替代批归一化、数据增强和对比损失的方法，在OAI数据集上显著提升分类性能。
摘要：
膝骨关节炎（KOA）是一种常见的关节疾病，会导致疼痛和行动不便。尽管基于MRI的深度学习模型在全膝关节置换（TKR）和疾病进展预测中表现出优越性能，但其泛化能力仍然具有挑战性，尤其是在应用于不同来源的影像数据时。在本研究中，我们证明用实例归一化替代批归一化、使用数据增强以及应用对比损失可以改善泛化能力。在训练和评估中，我们使用了来自骨关节炎倡议（OAI）数据库的MRI数据，将矢状面脂肪抑制中间加权涡轮自旋回波（FS-IW-TSE）图像作为源域，矢状面脂肪抑制三维（3D）双回波稳态（DESS）图像作为目标域。结果表明，通过在基线模型中将批归一化替换为实例归一化，使用全局强度非线性（GIN）增强方法生成增强输入视图，以及在分类损失之外加入监督对比损失以对齐相同标签样本的表征，两个域的分类指标均有统计学显著提升。当使用3D实例归一化时，带有对比损失的GIN方法优于所有评估的单源域泛化方法。比较有无对比损失的GIN（针对两种归一化类型）表明，添加对比损失始终带来更好的性能。
英文摘要：
Knee osteoarthritis (KOA) is a common joint disease that causes pain and mobility issues. While MRI-based deep learning models have demonstrated superior performance in predicting total knee replacement (TKR) and disease progression, their generalizability remains challenging, particularly when applied to imaging data from different sources. In this study, we show that replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss improves generalization. For training and evaluation, we used MRI data from the Osteoarthritis Initiative (OAI) database, considering sagittal fat-suppressed intermediate-weighted turbo spin-echo (FS-IW-TSE) images as the source domain and sagittal fat-suppressed three-dimensional (3D) dual-echo in steady state (DESS) images as the target domain. The results demonstrated a statistically significant improvement in classification metrics across both domains by replacing batch normalization with instance normalization in the baseline model, generating augmented input views using the Global Intensity Non-linear (GIN) augmentation method, and incorporating a supervised contrastive loss alongside the classification loss to align representations of samples with the same label. The GIN method with contrastive loss performed better than all evaluated single-source domain generalization methods when using 3D instance normalization. Comparing GIN with and without contrastive loss (for both normalization types) showed that adding contrastive loss consistently led to better performance.
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