BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images

Author:

Hellen Dominick J.1ORCID,Fay Meredith E.23,Lee David H.1,Klindt-Morgan Caroline1,Bennett Ashley1,Pachura Kimberly J.1,Grakoui Arash4,Huppert Stacey S.56,Dawson Paul A.1ORCID,Lam Wilbur A.23,Karpen Saul J.1ORCID

Affiliation:

1. Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Children’s Healthcare of Atlanta and Emory University School of Medicine, Atlanta, Georgia, United States

2. The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States

3. Department of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States

4. Emory National Primate Research Center, Division of Microbiology and Immunology, Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, United States

5. Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States

6. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States

Abstract

BiliQML is the first comprehensive machine-learning platform for biliary form analysis in whole slide histopathological images. This platform provides clinical and basic science researchers with a novel tool for the improved quantification and characterization of biliary tract disorders.

Funder

HHS | NIH | National Institute of Allergy and Infectious Diseases

HHS | NIH | National Institute of Biomedical Imaging and Bioengineering

HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases

HHS | NIH | National Institute of General Medical Sciences

Chan Zuckerberg Initiative

Deutsche Forschungsgemeinschaft

HHS | NIH | National Heart, Lung, and Blood Institute

Publisher

American Physiological Society

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Generative AI in Pediatric Gastroenterology;Current Gastroenterology Reports;2024-09-07

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