MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging

Chaojie Zhang, Shengjia Chen, Ozkan Cigdem, Haresh Rengaraj Rajamohan, Kyunghyun Cho +2 more
5/5/2024
eess.IVcs.CV

No official code found. Best implementation: denizlab/MR-Transformer (9 stars, last commit 2025-07-16).

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.

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Code Implementations(1)

denizlab/MR-TransformerCommunity100%
92Python, Jupyter NotebookMay 3, 20241 years agoNOASSERTION

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Cite this paper

@article{zhang2024mrtransformer,
  title  = {MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging},
  author = {Chaojie Zhang and Shengjia Chen and Ozkan Cigdem and Haresh Rengaraj Rajamohan and Kyunghyun Cho and Richard Kijowski and Cem M. Deniz},
  year   = {2024},
  eprint = {2405.02784},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.02784v1}
}

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