Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology
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ORIGINAL PAPER
Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology Soon Woo Kwon 1 & Ik Joon Choi 2 & Ju Yong Kang 2 & Won Il Jang 3 & Guk-Haeng Lee 2 & Myung-Chul Lee 2
# Society for Imaging Informatics in Medicine 2020
Abstract Ultrasonography with fine-needle aspiration biopsy is commonly used to detect thyroid cancer. However, thyroid ultrasonography is prone to subjective interpretations and interobserver variabilities. The objective of this study was to develop a thyroid nodule classification system for ultrasonography using convolutional neural networks. Transverse and longitudinal ultrasonographic thyroid images of 762 patients were used to create a deep learning model. After surgical biopsy, 325 cases were confirmed to be benign and 437 cases were confirmed to be papillary thyroid carcinoma. Image annotation marks were removed, and missing regions were recovered using neighboring parenchyme. To reduce overfitting of the deep learning model, we applied data augmentation, global average pooling. And 4-fold cross-validation was performed to detect overfitting. We employed a transfer learning method with the pretrained deep learning model VGG16. The average area under the curve of the model was 0.916, and its specificity and sensitivity were 0.70 and 0.92, respectively. Positive and negative predictive values were 0.90 and 0.75, respectively. We introduced a new fine-tuned deep learning model for classifying thyroid nodules in ultrasonography. We expect that this model will help physicians diagnose thyroid nodules with ultrasonography. Keywords Deep convolutional neural network . Deep learning . Ultrasonography . Thyroid nodule classification
Background The incidence of thyroid cancer has increased steeply worldwide over the past few decades [1]. The National Cancer Institute reported 56,870 new cases and 2010 thyroid cancer–specific deaths in 2017. According to the American Thyroid Association guidelines, ultrasonography with fine-needle aspiration biopsy is the main method of thyroid cancer detection [2]. Thyroid ultrasonography is real-time and noninvasive; however, it is Soon Woo Kwon and Ik Joon Choi contributed equally to this work. * Myung-Chul Lee [email protected] 1
Radiation Medicine Clinical Research Division, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, South Korea
2
Department of Otorhinolaryngology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), 75 Nowon-gil, Nowon-gu, Seoul 139-706, South Korea
3
Radiation Oncology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, South Korea
easily affected by echo perturbation and speckle noise. Further, there are various echo patterns of each thyroid nodule; thus, if ultrasonography is not performed by experienced physicians, it is prone to subjective interpretations and interobserver variabilities. To reduce these limitations and facilitate communication with other phys
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