MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging
This work addresses knee osteoarthritis diagnosis for medical applications, but it is incremental as it applies a known transformer architecture to a specific domain with pre-training.
The authors tackled the problem of predicting total knee replacement (TKR) from MRI scans by developing MR-Transformer, a vision transformer model that incorporates ImageNet pre-training and captures 3D spatial correlations, achieving state-of-the-art performance compared to existing deep learning models.
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial correlation from the MR images. The performance of the proposed model was compared to existing state-of-the-art deep learning models for knee injury diagnosis using MRI. Knee MR scans of four different tissue contrasts from the Osteoarthritis Initiative and Multicenter Osteoarthritis Study databases were utilized in the study. Experimental results demonstrated the state-of-the-art performance of the proposed model on TKR prediction using MRI.