A Local and Global Feature Disentangled Network: Toward Classification of Benign-Malignant Thyroid Nodules From Ultrasound Image
Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and...
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| Published in | IEEE transactions on medical imaging Vol. 41; no. 6; pp. 1497 - 1509 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas. |
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| AbstractList | Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas. Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas.Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas. |
| Author | Chen, Yang Yang, Kai-Fu Li, Yong-Jie Luo, Yan Ma, Bu-Yun Zhao, Shi-Xuan |
| Author_xml | – sequence: 1 givenname: Shi-Xuan orcidid: 0000-0001-7387-2799 surname: Zhao fullname: Zhao, Shi-Xuan email: 975086188@qq.com organization: Radiation Oncology Key Laboratory of Sichuan Province, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Yang surname: Chen fullname: Chen, Yang email: ChenYang@scu.edu.cn organization: Department of Ultrasound and the West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China – sequence: 3 givenname: Kai-Fu orcidid: 0000-0002-3696-5889 surname: Yang fullname: Yang, Kai-Fu email: yangkf@uestc.edu.cn organization: Radiation Oncology Key Laboratory of Sichuan Province, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Yan surname: Luo fullname: Luo, Yan email: luoyan@wchscu.cn organization: Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China – sequence: 5 givenname: Bu-Yun surname: Ma fullname: Ma, Bu-Yun email: mabuyundoctor@163.com organization: Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China – sequence: 6 givenname: Yong-Jie orcidid: 0000-0002-7395-3131 surname: Li fullname: Li, Yong-Jie email: liyj@uestc.edu.cn organization: Radiation Oncology Key Laboratory of Sichuan Province, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China |
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| SubjectTerms | attention mechanism Cancer Cancer screening classification Deep learning deep neural network Feature extraction Humans Image classification Medical screening Nodules Physicians Radiomics Task analysis Thyroid Thyroid cancer Thyroid gland thyroid nodule Thyroid Nodule - diagnostic imaging Thyroid Nodule - pathology Ultrasonic imaging Ultrasonography - methods Ultrasound Ultrasound image |
| Title | A Local and Global Feature Disentangled Network: Toward Classification of Benign-Malignant Thyroid Nodules From Ultrasound Image |
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