Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks

Eunjung Lee; Heonkyu Ha; Hye Jung Kim; Hee Jung Moon; Jung Hee Byon; Sun Huh; Jinwoo Son; Jiyoung Yoon; Kyunghwa Han; Jin Young Kwak

doi:10.1038/s41598-019-56395-x

Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks

Eunjung Lee, Heonkyu Ha, Hye Jung Kim, Hee Jung Moon, Jung Hee Byon, Sun Huh, Jinwoo Son, Jiyoung Yoon, Kyunghwa Han, Jin Young Kwak

Department of Medicine

Yonsei University

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.

Original language	English
Article number	19854
Journal	Scientific Reports
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41598-019-56395-x
State	Published - 1 Dec 2019

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abstract = "Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.",

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AU - Kim, Hye Jung

AU - Moon, Hee Jung

AU - Byon, Jung Hee

AU - Huh, Sun

AU - Son, Jinwoo

AU - Yoon, Jiyoung

AU - Han, Kyunghwa

AU - Kwak, Jin Young

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AB - Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.

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Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks

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