Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction
This work addresses a domain-specific challenge for medical imaging researchers and clinicians by improving model robustness in knee osteoarthritis prediction, though it is incremental as it builds on existing normalization and loss techniques.
The study tackled the problem of poor generalization in MRI-based deep learning models for predicting total knee replacement across different imaging sources by replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss, resulting in statistically significant improvements in classification metrics across domains.
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.