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Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Authors
 Su Yeon Ko  ;  Ji Hye Lee  ;  Jung Hyun Yoon  ;  Hyesun Na  ;  Eunhye Hong  ;  Kyunghwa Han  ;  Inkyung Jung  ;  Eun‐Kyung Kim  ;  Hee Jung Moon  ;  Vivian Y. Park  ;  Eunjung Lee  ;  Jin Young Kwak 
Citation
 HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK, Vol.41(4) : 885-891, 2019 
Journal Title
 HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK 
ISSN
 1043-3074 
Issue Date
2019
Keywords
convolutional neural network (CNN) ; deep learning ; thyroid cancer ; thyroid nodule ; ultrasound
Abstract
BACKGROUND: We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic performance of CNN with that of experienced radiologists. METHODS: Between May 2012 and February 2015, 589 thyroid nodules in 519 patients were diagnosed as benign or malignant by surgical excision. Experienced radiologists retrospectively reviewed the US of the thyroid nodules in a test set. CNNs were trained and tested using retrospective data of 439 and 150 US images, respectively. Diagnostic performances were compared between the two groups. RESULTS: Of the 589 thyroid nodules, 396 were malignant and 193 were benign. The area under the curve (AUC) for diagnosing thyroid malignancy was 0.805-0.860 for radiologists. The AUCs for diagnosing thyroid malignancy for the three CNNs were 0.845, 0.835, and 0.850. There was no significant difference in AUC between radiologists and CNNs. CONCLUSIONS: CNNs showed comparable diagnostic performance compared to experienced radiologists in differentiating thyroid malignancy on US.
Full Text
https://onlinelibrary.wiley.com/doi/full/10.1002/hed.25415
DOI
10.1002/hed.25415
Appears in Collections:
5. Research Institutes (연구소) > Others (기타) > 1. Journal Papers
Yonsei Authors
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/169451
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