Abstract
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.
Abstract (translated)
为了使用磁共振成像（MRI）进行全膝关节置换（TKR）预测，我们开发了一种基于Transformer的深度学习模型MR-Transformer。该模型包括ImageNet预训练，并捕获了来自MRI的3D空间关联。与使用MRI进行膝关节损伤诊断的现有最先进的深度学习模型进行比较。本研究利用了Osteoarthritis Initiative和Multicenter Osteoarthritis Study数据库中的四种不同组织对比的膝关节MRI数据。实验结果表明，基于MRI的TKR预测中，所提出的模型的性能达到了最先进水平。
URL
https://arxiv.org/abs/2405.02784
