Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

(2019) Charlie A. Hamm et al.   EUROPEAN RADIOLOGY

Nonstandardized Terminology to Describe Focal Liver Lesions in Patients at Risk for Hepatocellular Carcinoma: Implications Regarding Clinical Communication

(2018) Michael T. Corwin et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study

(2018) Kathryn J. Fowler et al.   RADIOLOGY

Evidence Supporting LI-RADS Major Features for CT- and MR Imaging–based Diagnosis of Hepatocellular Carcinoma: A Systematic Review

(2018) An Tang et al.   RADIOLOGY

LI-RADS: a glimpse into the future

(2018) Claude B. Sirlin et al.   Abdominal Radiology

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

(2018) Pranav Rajpurkar et al.   PLOS MEDICINE

Diagnostic accuracy of prospective application of the Liver Imaging Reporting and Data System (LI-RADS) in gadoxetate-enhanced MRI

(2017) Yeun-Yoon Kim et al.   EUROPEAN RADIOLOGY

Deep Learning: A Primer for Radiologists

(2017) Gabriel Chartrand et al.   RADIOGRAPHICS

Management implications and outcomes of LI-RADS-2, -3, -4, and -M category observations

(2017) Donald G. Mitchell et al.   Abdominal Radiology

Hepatocarcinogenesis and LI-RADS

(2017) Kazim H. Narsinh et al.   Abdominal Radiology

Reliability, Validity, and Reader Acceptance of LI-RADS—An In-depth Analysis

(2016) Borna K. Barth et al.   ACADEMIC RADIOLOGY

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

(2016) Hayit Greenspan et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

(2016) Marios Anthimopoulos et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Liver Imaging Reporting and Data System: Review of Ancillary Imaging Features

(2016) Irene Cruite et al.   SEMINARS IN ROENTGENOLOGY

Rate of observation and inter-observer agreement for LI-RADS major features at CT and MRI in 184 pathology proven hepatocellular carcinomas

(2016) Eric C. Ehman et al.   Abdominal Radiology

Classifying CT/MR findings in patients with suspicion of hepatocellular carcinoma: Comparison of liver imaging reporting and data system and criteria-free Likert scale reporting models

(2015) Yu-Dong Zhang et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

LI-RADS (Liver Imaging Reporting and Data System): Summary, discussion, and consensus of the LI-RADS Management Working Group and future directions

(2014) Donald G. Mitchell et al.   HEPATOLOGY

Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications

(2014) Mustafa R. Bashir et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Repeatability of Diagnostic Features and Scoring Systems for Hepatocellular Carcinoma by Using MR Imaging

(2014) Matthew S. Davenport et al.   RADIOLOGY