Imperial College London

ProfessorMarkThursz

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Professor of Hepatology. Head of Department
 
 
 
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Contact

 

+44 (0)20 3312 1903m.thursz

 
 
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Assistant

 

Ms Dawn Campbell +44 (0)20 3312 6454

 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Forlano:2020:10.1016/j.cgh.2019.12.025,
author = {Forlano, R and Mullish, BH and Giannakeas, N and Maurice, JB and Angkathunyakul, N and Lloyd, J and Tzallas, AT and Tsipouras, M and Yee, M and Thursz, MR and Goldin, RD and Manousou, P},
doi = {10.1016/j.cgh.2019.12.025},
journal = {Clinical Gastroenterology and Hepatology},
pages = {2081--2090.e9},
title = {High-Throughput, Machine Learning–Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease},
url = {http://dx.doi.org/10.1016/j.cgh.2019.12.025},
volume = {18},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AU - Forlano,R
AU - Mullish,BH
AU - Giannakeas,N
AU - Maurice,JB
AU - Angkathunyakul,N
AU - Lloyd,J
AU - Tzallas,AT
AU - Tsipouras,M
AU - Yee,M
AU - Thursz,MR
AU - Goldin,RD
AU - Manousou,P
DO - 10.1016/j.cgh.2019.12.025
EP - 2090
PY - 2020///
SN - 1542-3565
SP - 2081
TI - High-Throughput, Machine Learning–Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease
T2 - Clinical Gastroenterology and Hepatology
UR - http://dx.doi.org/10.1016/j.cgh.2019.12.025
UR - http://hdl.handle.net/10044/1/76585
VL - 18
ER -