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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 |
Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography 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 Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm Fr409 DEEP LEARNING FOR AUTOMATED QUANTIFICATION OF SMALL AND LARGE DROPLET MACROSTEATOSIS ON LIVER BIOPSY Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD Evaluation of pediatric hydronephrosis using deep learning quantification of fluid-to-kidney-area ratio by ultrasonography Liver stiffness quantification in biopsy-proven nonalcoholic fatty liver disease patients using shear wave elastography in comparison with transient elastography Role of renal Duplex ultrasonography in evaluation of hepatorenal syndrome |
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