ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise r...

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Bibliographic Details
Main Authors:	Lyndon Boone, Mahdi Biparva, Parisa Mojiri Forooshani, Joel Ramirez, Mario Masellis, Robert Bartha, Sean Symons, Stephen Strother, Sandra E. Black, Chris Heyn, Anne L. Martel, Richard H. Swartz, Maged Goubran
Format:	Article
Language:	English
Published:	Elsevier 2023-09-01
Series:	NeuroImage
Subjects:	Segmentation Generalizable deep learning Out-of-distribution data Benchmarking MRI Corruptions and artifacts
Online Access:	http://www.sciencedirect.com/science/article/pii/S1053811923004408

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http://www.sciencedirect.com/science/article/pii/S1053811923004408

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

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