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16-01-2024 | Magnetic Resonance Imaging

Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear

Authors: Jiaoju Wang, Jiewen Luo, Jiehui Liang, Yangbo Cao, Jing Feng, Lingjie Tan, Zhengcheng Wang, Jingming Li, Alphonse Houssou Hounye, Muzhou Hou, Jinshen He

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2024

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Abstract

Anterior cruciate ligament (ACL) tears are prevalent orthopedic sports injuries and are difficult to precisely classify. Previous works have demonstrated the ability of deep learning (DL) to provide support for clinicians in ACL tear classification scenarios, but it requires a large quantity of labeled samples and incurs a high computational expense. This study aims to overcome the challenges brought by small and imbalanced data and achieve fast and accurate ACL tear classification based on magnetic resonance imaging (MRI) of the knee. We propose a lightweight attentive graph neural network (GNN) with a conditional random field (CRF), named the ACGNN, to classify ACL ruptures in knee MR images. A metric-based meta-learning strategy is introduced to conduct independent testing through multiple node classification tasks. We design a lightweight feature embedding network using a feature-based knowledge distillation method to extract features from the given images. Then, GNN layers are used to find the dependencies between samples and complete the classification process. The CRF is incorporated into each GNN layer to refine the affinities. To mitigate oversmoothing and overfitting issues, we apply self-boosting attention, node attention, and memory attention for graph initialization, node updating, and correlation across graph layers, respectively. Experiments demonstrated that our model provided excellent performance on both oblique coronal data and sagittal data with accuracies of 92.94% and 91.92%, respectively. Notably, our proposed method exhibited comparable performance to that of orthopedic surgeons during an internal clinical validation. This work shows the potential of our method to advance ACL diagnosis and facilitates the development of computer-aided diagnosis methods for use in clinical practice.
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Metadata
Title
Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear
Authors
Jiaoju Wang
Jiewen Luo
Jiehui Liang
Yangbo Cao
Jing Feng
Lingjie Tan
Zhengcheng Wang
Jingming Li
Alphonse Houssou Hounye
Muzhou Hou
Jinshen He
Publication date
16-01-2024
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 2/2024
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-023-00944-4

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