Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images

doi:10.1109/TBME.2020.2971065

	Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images
	Zhou, Hui1,2 ; Wang, Kun1,2 ; Tian, Jie 1,2,3
发表期刊	IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
ISSN	0018-9294
	2020-10-01
卷号	67 期号:10 页码:2773-2780
通讯作者	Tian, Jie(jie.tian@ia.ac.cn)
摘要	Objective: We aimed to propose a highly automatic and objective model named online transfer learning (OTL) for the differential diagnosis of benign and malignant thyroid nodules from ultrasound (US) images. Methods: The OTL mothed combined the strategy of transfer learning and online learning. Two datasets (1750 thyroid nodules with 1078 benign and 672 malignant nodules, and 3852 thyroid nodules with 3213 benign and 639 malignant nodules) were collected to develop the model. The diagnostic accuracy was also compared with VGG-16 based transfer learning model and different input images based model. Analysis of receiver operating characteristic (ROC) curves were performed to calculate optimal area under it (AUC) for benign and malignant nodules. Results: AUC, sensitivity and specificity of OTL were 0.98 (95% confidence interval [CI]: 0.97-0.99), 98.7% (95% confidence interval [CI]: 97.8%-99.6%) and 98.8% (95% confidence interval [CI]: 97.9%-99.7%) in the final online learning step, which was significantly better than other deep learning models (P < 0.01). Conclusion: OTL model shows the best overall performance comparing with other deep learning models. The model holds a good potential for improving the overall diagnostic efficacy in thyroid nodule US examinations. Significance: The proposed OTL model could be seamlessly integrated into the conventional work-flow of thyroid nodule US examinations.
关键词	Cancer Biological system modeling Ultrasonic imaging Feature extraction Radiomics Training Deep learning Diagnosis online learning radiomics transfer learning thyroid nodules ultrasound images
DOI	10.1109/TBME.2020.2971065
关键词[WOS]	FEATURES ; RISK ; US ; CLASSIFICATION ; MANAGEMENT ; CARCINOMA ; CANCER
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[81930053] ; Chinese Academy of Sciences[YJKYYQ2018 0048] ; Chinese Academy of Sciences[XDB32030200]
项目资助者	Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical
WOS记录号	WOS:000571741600007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：27[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42007
专题	中国科学院分子影像重点实验室
通讯作者	Tian, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Beijing Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
第一作者单位	中国科学院分子影像重点实验室
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 GB/T 7714	Zhou, Hui,Wang, Kun,Tian, Jie. Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2020,67(10):2773-2780.
APA	Zhou, Hui,Wang, Kun,&Tian, Jie.(2020).Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,67(10),2773-2780.
MLA	Zhou, Hui,et al."Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid Nodules With Ultrasound Images".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 67.10(2020):2773-2780.