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Found 3 papers, 2 papers with code
Reducing Textural Bias Improves Robustness of Deep Segmentation Models
In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data.
Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain
Standard practice for using convolutional neural networks (CNNs) in semantic segmentation tasks assumes that the image intensities are directly used for training and inference.
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
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