Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network-Reference-Cited by-同舟云学术

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Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

Published:2019-09-02 Issue:2 Volume:30 Page:1264-1273
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Lee Jeong Hyun,Joo Ijin,Kang Tae Wook,Paik Yong Han,Sinn Dong Hyun,Ha Sang Yun,Kim Kyunga,Choi Choonghwan,Lee Gunwoo,Yi Jonghyon,Bang Won-Chul

Funder

Samsung Medical Center

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s00330-019-06407-1.pdf

Reference39 articles.

1. GBD 2013 Mortality and Causes of Death Collaborators (2015) Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385:117–171

2. Manning DS, Afdhal NH (2008) Diagnosis and quantitation of fibrosis. Gastroenterology 134:1670–1681

3. Vergniol J, Foucher J, Terrebonne E et al (2011) Noninvasive tests for fibrosis and liver stiffness predict 5-year outcomes of patients with chronic hepatitis C. Gastroenterology 140:1970–1979 1979 e1971–1973

4. Seeff LB, Everson GT, Morgan TR et al (2010) Complication rate of percutaneous liver biopsies among persons with advanced chronic liver disease in the HALT-C trial. Clin Gastroenterol Hepatol 8:877–883

5. Stotland BR, Lichtenstein GR (1996) Liver biopsy complications and routine ultrasound. Am J Gastroenterol 91:1295–1296

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