niftynet.network.scalenet module¶
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class
ScaleNet
(num_classes, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, acti_func='prelu', name='ScaleNet')[source]¶ Bases:
niftynet.network.base_net.BaseNet
- implementation of ScaleNet:
- Fidon et al., “Scalable multimodal convolutional networks for brain tumour segmentation”, MICCAI ‘17
### Diagram
INPUT –> [BACKEND] —-> [MERGING] —-> [FRONTEND] —> OUTPUT
[BACKEND] and [MERGING] are provided by the ScaleBlock below [FRONTEND]: it can be any NiftyNet network (default: HighRes3dnet)
### Constraints: - Input image size should be divisible by 8 - more than one modality should be used
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__init__
(num_classes, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, acti_func='prelu', name='ScaleNet')[source]¶ Parameters: - num_classes – int, number of channels of output
- w_initializer – weight initialisation for network
- w_regularizer – weight regularisation for network
- b_initializer – bias initialisation for network
- b_regularizer – bias regularisation for network
- acti_func – activation function to use
- name – layer name
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class
ScaleBlock
(func, n_layers=1, w_initializer=None, w_regularizer=None, acti_func='relu', name='scaleblock')[source]¶ Bases:
niftynet.layer.base_layer.TrainableLayer
Implementation of the ScaleBlock described in Fidon et al., “Scalable multimodal convolutional
networks for brain tumour segmentation”, MICCAI ‘17See Fig 2(a) for diagram details - SN BackEnd
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__init__
(func, n_layers=1, w_initializer=None, w_regularizer=None, acti_func='relu', name='scaleblock')[source]¶ Parameters: - func – merging function (SUPPORTED_OP: MAX, AVERAGE)
- n_layers – int, number of layers
- w_initializer – weight initialisation for network
- w_regularizer – weight regularisation for network
- acti_func – activation function to use
- name – layer name
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