Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid
Zhou, Hui; Wang, Kun; Tian, Tian
Source PublicationIEEE Transactions on Biomedical Engineering
2020
Issue0Pages:0
Abstract

Abstract—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.
 

KeywordDiagnosis Ultrasound Images Online Learning Radiomics Transfer Learning Thyroid Nodules
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38567
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Corresponding AuthorTian, Tian
AffiliationCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Hui,Wang, Kun,Tian, Tian. Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid[J]. IEEE Transactions on Biomedical Engineering,2020(0):0.
APA Zhou, Hui,Wang, Kun,&Tian, Tian.(2020).Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid.IEEE Transactions on Biomedical Engineering(0),0.
MLA Zhou, Hui,et al."Online Transfer Learning for Differential Diagnosis of Benign and Malignant Thyroid".IEEE Transactions on Biomedical Engineering .0(2020):0.
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