Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images-Reference-Cited by-同舟云学术

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Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images

Published:2022-11 Issue: Volume:150 Page:106089
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Yu Hanyi^ORCID,Sharifai Nima,Jiang Kun,Wang Fusheng,Teodoro George,Farris Alton B.,Kong Jun

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

National Science Foundation

National Institutes of Health

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference50 articles.

1. Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients;Jiang;PLoS One,2020

2. Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images;Yasaka;Radiology,2018

3. An appraisal of the histopathological assessment of liver fibrosis;Standish;Gut,2006

4. Progression and regression of fibrosis in viral hepatitis in the treatment era: the Beijing classification;Theise;Modern Pathol.,2018

5. Deep learning enables automated scoring of liver fibrosis stages;Yu;Sci. Rep.,2018

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