Deep learning for NeuroImaging in Python.
Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.
- class surfify.models.unet.SphericalGUNet(in_channels, out_channels, input_dim=192, depth=5, start_filts=32)[source]¶
The Spherical Grided U-Net architecture.
The architecture is built upon specific spherical surface convolution, pooling, and transposed convolution modules. It has an encoder path and a decoder path, with a user-defined resolution steps. Different from the standard U-Net, all 3x3 convolution are replaced with the SpMa convolution. In addition to the standard U-Net, before each convolution layer’s rectified linear units (ReLU) activation function, a batch normalization layer is added. The number of feature channels are double after each surface pooling layer and halve at each transposed convolution or up sampling layer.
See also
Notes
Debuging messages can be displayed by changing the log level using
setup_logging(level='debug').References
Zhao F, et al., Spherical U-Net on Cortical Surfaces: Methods and Applications, IPMI, 2019.
Init SphericalUNet.
- Parameters:
in_channels : int
input features/channels.
out_channels : int
output features/channels.
input_dim : int, default 192
the size of the converted 3-D surface to the 2-D grid.
depth : int, default 5
number of layers in the UNet.
start_filts : int, default 32
number of convolutional filters for the first conv.
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