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

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

:param dataset_old: :param dataset_new: :return:

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

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

filenames must be a list of strings, each pointing to a separate 3d tiff file. One file per modality. If your data only has one imaging modal

I believe they want .nii, not .nii.gz :param source_dir: :param target_dir: :return:

output_folder is where the extracted template is :param folder_with_predictions: :param output_folder: :return:

this is preparation for ensembling :return:

does not rely on dataset.json :param folder: :return:

img must be [c, x, y] img[-1] must be the segmentation with segmentation>0 being foreground :param img: :param crop_size: :param

2TP / (2TP + FP + FN)

This is a suggestion for if your dataset is a dictionary (my personal standard) :return:

The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded, so always the same) and

The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded, so always the same) and

Only vertebra that are present in the reference will be evaluated :param sitk_file_ref: :param sitk_file_test: :return:

they expect us to submit a 32 bit 3d tif image with values between 0 (100% membrane certainty) and 1 (100% non-membrane certainty). We use th

promise wants mhd :-/ :param source_dir: :param target_dir: :return:

FP / (TP + FP)

FN / (TP + FN)

FP / (FP + TN)