Enhancing convolutional neural network scheme for rheumatoid arthritis grading with limited clinical data

作     者：Jian Tang Zhibin Jin Xue Zhou Weijing Zhang Min Wu Qinghong Shen Qian Cheng Xueding Wang Jie Yuan 汤键;金志斌;周雪;张玮婧;吴敏;沈庆宏;程茜;王学鼎;袁杰

作者机构：School of Electronic Science and EngineeringNanjing University Nanjing Drum Tower Hospital Institution of AcousticsTongji University 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2019年第28卷第3期

页      面：391-398页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by the National Key Research and Development Program of China(Grant No.2017YFC0111402) the Natural Science Funds of Jiangsu Province of China(Grant No.BK20181256) 

主　　题：rheumatoid arthritis convolutional neural network medical ultrasound images 

摘      要：The gray-scale ultrasound(US) imaging method is usually used to assess synovitis in rheumatoid arthritis(RA) in clinical practice. This four-grade scoring system depends highly on the sonographer s experience and has relatively lower validity compared with quantitative indexes. However, the training of a qualified sonographer is expensive and timeconsuming while few studies focused on automatic RA grading methods. The purpose of this study is to propose an automatic RA grading method using deep convolutional neural networks(DCNN) to assist clinical assessment. Gray-scale ultrasound images of finger joints are taken as inputs while the output is the corresponding RA grading results. Firstly,we performed the auto-localization of synovium in the RA image and obtained a high precision in localization. In order to make up for the lack of a large annotated training dataset, we performed data augmentation to increase the number of training samples. Motivated by the approach of transfer learning, we pre-trained the GoogLeNet on ImageNet as a feature extractor and then fine-tuned it on our own dataset. The detection results showed an average precision exceeding 90%. In the experiment of grading RA severity, the four-grade classification accuracy exceeded 90% while the binary classification accuracies exceeded 95%. The results demonstrate that our proposed method achieves performances comparable to RA experts in multi-class classification. The promising results of our proposed DCNN-based RA grading method can have the ability to provide an objective and accurate reference to assist RA diagnosis and the training of sonographers.