Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

• Published in: European radiology Vol. 31; no. 11; pp. 8807 - 8815
• Main Authors: Nowak, Sebastian, Mesropyan, Narine, Faron, Anton, Block, Wolfgang, Reuter, Martin, Attenberger, Ulrike I., Luetkens, Julian A., Sprinkart, Alois M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Accuracy; Archives & records; Artificial neural networks; Chi-square test; Cirrhosis; Classification; Coders; Deep learning; Diagnosis; Diagnostic Radiology; Female; Humans; Image classification; Image Processing, Computer-Assisted; Image segmentation; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Learning; Liver; Liver cirrhosis; Liver Cirrhosis - diagnostic imaging; Liver diseases; Machine Learning; Magnetic Resonance Imaging; Male; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Neuroradiology; Radiology; Retrospective Studies; Statistical tests; Transfer learning; Ultrasound; Upstream; Deep learning; Neural networks, computer; Magnetic resonance imaging; Liver cirrhosis

Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects...