3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient. (arXiv:2201.03644v1 [eess.IV])

The convolutional layer and loss function are two fundamental components in
deep learning. Because of the success of conventional deep learning kernels,
the less versatile Gabor kernels become less popular despite the fact that they
can provide abundant features at different frequencies, orientations, and
scales with much fewer parameters. For existing loss functions for multi-class
image segmentation, there is usually a tradeoff among accuracy, robustness to
hyperparameters, and manual weight selections for combining different losses.
Therefore, to gain the benefits …

arxiv correlation segmentation