复杂系统管理与控制国家重点实验室知识库

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.