Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study

[1] Fowler KJ(2018)Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study Radiology 286 173-185

[2] Tang A(2018)Interobserver and intermodality agreement of standardized algorithms for non-invasive diagnosis of hepatocellular carcinoma in high-risk patients: CEUS-LI-RADS versus MRI-LI-RADS European radiology 28 4254-4264

[3] Santillan C(2016)Reliability, Validity, and Reader Acceptance of LI-RADS-An In-depth Analysis Academic radiology 23 1145-1153

[4] Bhargavan-Chatfield M(2014)Repeatability of diagnostic features and scoring systems for hepatocellular carcinoma by using MR imaging Radiology 272 132-142

[5] Heiken J(2016)Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Jama 316 2402-2410

[6] Jha RC(2017)Dermatologist-level classification of skin cancer with deep neural networks Nature 542 115-118

[7] Weinreb J(2017)Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Jama 318 2199-2210

[8] Hussain H(2018)Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study Lancet (London, England) 392 2388-2396

[9] Mitchell DG(2018)Clinically applicable deep learning for diagnosis and referral in retinal disease Nature medicine 24 1342-1350

[10] Bashir MR(2015)ImageNet Large Scale Visual Recognition Challenge International Journal of Computer Vision 115 211-252

[1] Fowler KJ(2018)Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study Radiology 286 173-185

[2] Tang A(2018)Interobserver and intermodality agreement of standardized algorithms for non-invasive diagnosis of hepatocellular carcinoma in high-risk patients: CEUS-LI-RADS versus MRI-LI-RADS European radiology 28 4254-4264

[3] Santillan C(2016)Reliability, Validity, and Reader Acceptance of LI-RADS-An In-depth Analysis Academic radiology 23 1145-1153

[4] Bhargavan-Chatfield M(2014)Repeatability of diagnostic features and scoring systems for hepatocellular carcinoma by using MR imaging Radiology 272 132-142

[5] Heiken J(2016)Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Jama 316 2402-2410

[6] Jha RC(2017)Dermatologist-level classification of skin cancer with deep neural networks Nature 542 115-118

[7] Weinreb J(2017)Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Jama 318 2199-2210

[8] Hussain H(2018)Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study Lancet (London, England) 392 2388-2396

[9] Mitchell DG(2018)Clinically applicable deep learning for diagnosis and referral in retinal disease Nature medicine 24 1342-1350

[10] Bashir MR(2015)ImageNet Large Scale Visual Recognition Challenge International Journal of Computer Vision 115 211-252