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- Title
基于Transformer增强卷积的 膝关节磁共振影像年龄预测.
- Authors
朱昊哲; 邓小冬爲; 廖培希彳; 杜文超; 陈怀歆°; 刘 洪叹; 陈 虎; 邓振华; 爲杨红; 雨I越
- Abstract
Age estimation is regarded as a crucial topic and a very active research field in clinical medicine, Recently, due to the drawback o£ ionizing radiation from the traditional imageological examination, growing more and more studies have focused on using magnetic resonance imaging (MRI) for bone age prediction. This paper proposes a novel end-tcrend network based on the knee MRI dataset, which combines the convolution neural network (CNN) and Masked-Transformer network to extract complementary features, and uses a feature aggregation module to aggregate features of different local knee MRI slices. By integrating the feature maps of CNN and. the patch embeddings of visual transformer branches, the feature extraction module can complementarily acquire local and global information to better extract age-related features. A feature aggregation module composed of the graph attention network is proposed in our work to integrate the local features of different MRI slices at the feature level to achieve the interaction between multiple slice features. Extensive experiments demonstrate that our method can achieve state-of-the-art performance in the knee MRI age estimation task. Specifically» our method is tested on a dataset including 44 knee MRI samples aging from 12. 0 to 25. 9 years, and the best result of fiverfold cross-validation is a mean absolute error of 1. 57 士 1. 34 years in age regression.
- Subjects
CONVOLUTIONAL neural networks; CLINICAL medicine research; MAGNETIC resonance imaging; FEATURE extraction; IONIZING radiation
- Publication
Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban, 2023, Vol 60, Issue 5, p210
- ISSN
0490-6756
- Publication type
Article
- DOI
10.19907/j.0490-6756.2023.052001