Quantification of hepatic steatosis in histologic images by deep learning method-Reference-Cited by-同舟云学术

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Quantification of hepatic steatosis in histologic images by deep learning method

Published:2020-01-02 Issue:6 Volume:27 Page:1033-1045
ISSN:0895-3996
Container-title:Journal of X-Ray Science and Technology
language:
Short-container-title:XST

Author:

Yang Fan¹²,Jia Xianyuan¹²,Lei Pinggui³,He Yan¹²,Xiang Yining⁴,Jiao Jun³,Zhou Shi³,Qian Wei⁵,Duan Qinghong³

Affiliation:

1. School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China

2. Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China

3. Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China

4. Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China

5. Department of Electrical and Computer Engineering, College of Engineering, University of Texas, El Paso, TX, USA

Publisher

IOS Press

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiology Nuclear Medicine and imaging,Instrumentation,Radiation

Reference46 articles.

1. The burden of liver disease in Europe: A review of available epidemiological data;Blachier;J Hepatol,2013

2. Current histological classification of NAFLD: Strength and limitations;Bedossa;Hepatol Int,2013

3. Histological assessment of NAFLD;Bedossa;Dig Dis Sci,2016

4. Definitions of normal liver fat and the association of insulin sensitivity with acquired and genetic NAFLD-A systematic review;Petäjä;Int J Mol Sci,2016

5. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD;Hagström;J Hepatol,2017

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