network: "2d-unet", "3d-unet", "2d-dense", "3d-dense", "3d-dense-compact"
loss: "focal_loss", "fbeta_loss", "dice_loss", "categorical_crossentropy", "generalized_dice_loss"
learning_rate: ..., "0.01", "0.005", "0.001", ...
decay_rate: "0.95", "0.9", ...
(decay for learning rate after each 500 epochs)
gpu: "0", "1", "2", "3", "0,1", "0,2", ..., "0,1,2", "0,1,3", ..., "0,1,2,3"
(selection of GPUs to be visible to be program)
dimension_size_X: ..., "64", "128", "256", "512", ...
(dimension sizes of the patches)
final_image_shape: (XXX,XXX,XX)
(shape of the disired resampled images - no effect if "resample" is "false")
sequences: "1", ...
(number of image modalities)
classes: "1", ...
(number of prediction classes)
batch_size: "1", ...
(number of batches for training)
raw_format: "nrrd" or "nii"
(original format of images)
resample: "true" or "false"
patch_wise: "true" or "false"
model_name: "XXXXX.hdf5" or "XXXXX.hd5"
(model name located in the model folder)