Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation
Radiology Artificial Intelligence2023Vol. 5(3), pp. e220080–e220080
Citations Over TimeTop 22% of 2023 papers
Brandon Konkel, Jacob A. Macdonald, Kyle J. Lafata, Islam H. Zaki, Erol Bozdoĝan, Mohammad Rauf Chaudhry, Yuqi Wang, Gemini Janas, Walter F. Wiggins, Mustafa R. Bashir
Abstract
Domain shift in liver segmentation appears to be tied to variations in soft-tissue contrast and can be effectively bridged with diversification of soft-tissue representation in training data.Keywords: Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning, CT, MRI, Liver Segmentation Supplemental material is available for this article. © RSNA, 2023.
Related Papers
- → Generalizing Generalizability in Information Systems Research(2003)1,544 cited
- → Generalizability theory: a primer(1992)1,325 cited
- → Applying Generalizability Theory using EduG(2011)108 cited
- → Reliability of observers' subjective impressions of families: A generalizability theory approach(2012)20 cited
- → Using Generalizability Theory for the Estimation of Reliability of a Patient Classification System(1994)1 cited