changed deep supervision to False :return:

initialize self.optimizer and self.lr_scheduler (if applicable) here :return:

TP / (TP + FP + FN)

used for if the checkpoint is already in ram :param checkpoint: :param train: :return:

This was not used. I was just curious because others used this. Turns out this is not necessary for my networks

ALSO NOT USED! :return:

here we go one step, then use polyLR :param epoch: :return:

TN / (TN + FN)

changes best checkpoint pickle nnunettrainer class name to nnUNetTrainer :param experiments: :return:

This implementation differs from the official surface dice implementation! These two are not comparable!!!!! The normalized surface dice is

overwrite patient-based early stopping. Always run to 1000 epochs :return:

overwrite patient-based early stopping. Always run to 1000 epochs :return:

DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres :return:

DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres :return:

segment with lung mask, get bbox from that, use bbox to remove predictions in background WE EXPERIMENTED WITH THAT ON THE VALIDATION SET AND

Use this function to predict a 2D image. If this is a 3D U-Net it will crash because you cannot predict a 2D image with that (you dum

We need to wrap this because we need to enforce self.network.do_ds = False for prediction

We need to wrap this because we need to enforce self.network.do_ds = False for prediction

Use this to predict new data :param input_files: :param output_file: :param softmax_ouput_file: :param mixed_

Use this to predict new data :param input_files: :param output_file: :param softmax_ouput_file: :param mixed_

This was not used. I was just curious because others used this. Turns out this is not necessary for my networks

Will overwrite image!!! :param image: :return:

data and seg must already have been transposed by transpose_forward. properties are the un-transposed values (spacing etc) :p