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Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography
Dong Ik Cha, Tae Wook Kang, Ji Hye Min, Ijin Joo, Dong Hyun Sinn, Sang Yun Ha, Kyunga Kim, Gunwoo Lee, Jonghyon Yi
Ultrasonography. 2021;40(4):565-574.   Published online 2021 February 24    DOI: https://doi.org/10.14366/usg.20179

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Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography
Ultrasonography. 2021;40(4):565-574   Crossref logo
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Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography
BioMed Research International. 2015;2015:1-9   Crossref logo
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383 A COMPARISON OF FATTY LIVER INDEX WITH THREE NON-INVASIVE METHODS FOR THE DETECTION AND QUANTIFICATION OF LIVER STEATOSIS: REGULAR ULTRASOUND, HEPATORENAL ULTRASOUND INDEX AND STEATOTEST
Journal of Hepatology. 2010;52:S158   Crossref logo
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Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice
Toxicologic Pathology. 2020;48(5):702-707   Crossref logo
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Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm
Toxicologic Pathology. 2021;49(5):1126-1133   Crossref logo
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Fr409 DEEP LEARNING FOR AUTOMATED QUANTIFICATION OF SMALL AND LARGE DROPLET MACROSTEATOSIS ON LIVER BIOPSY
Gastroenterology. 2021;160(6):S-813   Crossref logo
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Evaluation of pediatric hydronephrosis using deep learning quantification of fluid-to-kidney-area ratio by ultrasonography
Abdominal Radiology. 2021;46(11):5229-5239   Crossref logo
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Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD
Biocybernetics and Biomedical Engineering. 2018;38(1):145-157   Crossref logo
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Liver stiffness quantification in biopsy-proven nonalcoholic fatty liver disease patients using shear wave elastography in comparison with transient elastography
Ultrasonography. 2021;40(3):407-416   Crossref logo
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Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture
Journal of Magnetic Resonance Imaging. 2017;47(3):723-728   Crossref logo
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