Reducing CNN Textural Bias With k-Space Artifacts Improves Robustness

Reducing CNN Textural Bias With k-Space Artifacts Improves Robustness

Convolutional neural networks (CNNs) have become the <italic>de facto</italic> algorithms of choice for semantic segmentation tasks in biomedical image processing. Yet, models based on CNNs remain susceptible to the domain shift problem, where a mismatch between source and target distrib...

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Bibliographic Details
Main Authors: Yaniel Cabrera, Ahmed E. Fetit
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Texture
bias
artifacts
robustness
MRI
CNNs
Online Access:https://ieeexplore.ieee.org/document/9786829/

Internet

https://ieeexplore.ieee.org/document/9786829/

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