:param dont_do_if_covers_more_than_X_percent: dont_do_if_covers_more_than_X_percent=0.25 is 25\%! :param channel_idx: can be list or

2019_11_22: I have no idea what the purpose of this was... the same as above but here we should use only expanding operations. Expan

data[mask < 0] = 0 Sets everything outside the mask to 0. CAREFUL! outside is defined as < 0, not =0 (in the Mask)!!! :param

regions are tuple of tuples where each inner tuple holds the class indices that are merged into one region, example: regions= ((1, 2)

based on matthews correlation coefficient https://en.wikipedia.org/wiki/Matthews_correlation_coefficient Does not work. Real

squares the terms in the denominator as proposed by Milletari et al.

CAREFUL. Weights for CE and Dice do not need to sum to one. You can set whatever you want. :param soft_dice_kwargs: :param ce

DO NOT APPLY NONLINEARITY IN YOUR NETWORK! THIS LOSS IS INTENDED TO BE USED FOR BRATS REGIONS ONLY :param soft_dice_kwargs:

use this if you have several outputs and ground truth (both list of same len) and the loss should be computed between them (x[0] and

stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers

basically more flexible than v1, architecture is the same Does this look complicated? Nah bro. Functionality > usability Th

stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers

basically more flexible than v1, architecture is the same Does this look complicated? Nah bro. Functionality > usability Th

Following UNet building blocks can be added by utilizing the properties this class exposes (TODO) this one includes the bottleneck l

Following UNet building blocks can be added by utilizing the properties this class exposes (TODO) this one includes the bottleneck l

As opposed to the Generic_UNet, this class will compute parts of the loss function in the forward pass. This is useful for GPU parall

stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers

basically more flexible than v1, architecture is the same Does this look complicated? Nah bro. Functionality > usability Th

stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers

basically more flexible than v1, architecture is the same Does this look complicated? Nah bro. Functionality > usability Th

if network_props['dropout_op'] is None then no dropout if network_props['norm_op'] is None then no norm :param input_channels

if network_props['dropout_op'] is None then no dropout if network_props['norm_op'] is None then no norm :param input_channels

This is the conv bn nonlin conv bn nonlin kind of block :param in_planes: :param out_planes: :param props: :p

This is the conv bn nonlin conv bn nonlin kind of block :param in_planes: :param out_planes: :param props: :p

1) what class is in this training case? 2) what is the size distribution for each class? 3) what is the region size of each c

(TP + TN) / (TP + FP + FN + TN)

We need this for 2D dataloader with oversampling. As of now it will detect slices that contain specific classes at run time, meaning it needs

The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3,

The identity block is the block that has no conv layer at shortcut. # Arguments kernel_size: default 3, the kernel size of mid

collect all cv_niftis, compute brats metrics, compute enh tumor thresholds and summarize in csv :param base_dir: :return:

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t

This only applies for num_blocks_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t