This is the basic data loader for 3D networks. It uses preprocessed data as produced by my (Fabian) preprocessing. You can load the d

This one does fully convolutional inference. No sliding window

inspired by https://stackoverflow.com/questions/1855095/how-to-create-a-zip-archive-of-a-directory-in-python

:param directory: must have the 5 fold_X subfolders as well as a postprocessing.json and plans.pkl :param output_directory: :return:

This script will run recursively through all subfolders of current_folder and copy all files that end with suffix with some automatically gen

:param data: :param seg: :param nonzero_label: this will be written into the segmentation map :return:

attempting to reimplement the brats evaluation scheme assumes edema=1, non_enh=2, enh=3 :param arr: :param arr_gt: :return:

FN / (TN + FN)

Can be implemented, does not have to :return:

replace genericUNet with the implementation of above for super speeds

- momentum 0.99 - SGD instead of Adam - self.lr_scheduler = None because we do poly_lr - deep supervision = True

- momentum 0.99 - SGD instead of Adam - self.lr_scheduler = None because we do poly_lr - deep supervision = True

This is specific to the U-Net and must be adapted for other network architectures :return:

changed deep supervision to False :return:

changed deep supervision to False :return:

inference_apply_nonlin to sigmoid

inference_apply_nonlin to sigmoid