Ultrasound-based thyroid follicular neoplasm classification using fine-tuning and graph convolutional network

Zaoming Yan; Bailin Yang; Fangfang Dong; Meiying Yan; Dong Xu; Changrui Zhu; Guohua Cheng; Linyang He

doi:10.1117/12.2589377

27 January 2021 Ultrasound-based thyroid follicular neoplasm classification using fine-tuning and graph convolutional network

Zaoming Yan, Bailin Yang, Fangfang Dong, Meiying Yan, Dong Xu, Changrui Zhu, Guohua Cheng, Linyang He

Author Affiliations +

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201O (2021) https://doi.org/10.1117/12.2589377
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

The proposed method aims to identify thyroid follicular adenoma (TFA) and carcinoma (FTC) in ultrasound images. Although deep learning methods are powerful for image classification, it is limited for the small dataset from these two diseases. In this paper, we conduct the classification with fine-tuning and semi-supervised graph convolutional networks (GCN). First, we use a semi-automatic phase consistency geodesic active contour (PCGAC) method to segment the lesion areas. Then, by the fine-tuned EfficientNet, we extract the feature vectors. After that, the feature vectors are built as a graph. Finally, with the established graph, we utilize the semi-supervised GCN to classify TFA and FTC. The experimental results show the proposed method can recognize thyroid follicular neoplasm with 92.42%, 94.73% for specificity, 89.28% for sensitivity. Furthermore, the generalization ability is validated by three different testing data sets.

Citation Download Citation

Zaoming Yan, Bailin Yang, Fangfang Dong, Meiying Yan, Dong Xu, Changrui Zhu, Guohua Cheng, and Linyang He "Ultrasound-based thyroid follicular neoplasm classification using fine-tuning and graph convolutional network", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201O (27 January 2021); https://doi.org/10.1117/12.2589377

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