Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study.

[1]  S. Mandel,et al.  The Diagnosis and Management of Thyroid Nodules: A Review , 2018, JAMA.

[2]  Adam M. Alessio,et al.  Comparison of machine learned approaches for thyroid nodule characterization from shear wave elastography images , 2018, Medical Imaging.

[3]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[4]  William D Middleton,et al.  ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. , 2018, Journal of the American College of Radiology : JACR.

[5]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[6]  Raymond Y Huang,et al.  Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging , 2017, Clinical Cancer Research.

[7]  A. Samir,et al.  Imaging and Screening of Thyroid Cancer. , 2017, Radiologic clinics of North America.

[8]  Gilles Russ,et al.  European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS , 2017, European Thyroid Journal.

[9]  Hong Zhou,et al.  Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach , 2017, Comput. Methods Programs Biomed..

[10]  N. Rodondi,et al.  Overdiagnosis and overtreatment of thyroid cancer: A population-based temporal trend study , 2017, PloS one.

[11]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[12]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jinlian Ma,et al.  A pre‐trained convolutional neural network based method for thyroid nodule diagnosis , 2017, Ultrasonics.

[14]  A. Jemal,et al.  Cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[15]  Kaliszewski,et al.  American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer : The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer , 2017 .

[16]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[17]  Hyunsoon Cho,et al.  Association between screening and the thyroid cancer “epidemic” in South Korea: evidence from a nationwide study , 2016, British Medical Journal.

[18]  H. Gharib,et al.  Thyroid nodule update on diagnosis and management , 2016, Clinical Diabetes and Endocrinology.

[19]  A. Jemal,et al.  Cancer statistics in China, 2015 , 2016, CA: a cancer journal for clinicians.

[20]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  S. Mandel,et al.  2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. , 2009, Thyroid : official journal of the American Thyroid Association.

[23]  Namkug Kim,et al.  Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments. , 2016, Medical physics.

[24]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[25]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[26]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[27]  E. S. Pearson,et al.  THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL , 1934 .