config_segmentation_metrics_brats_concise.yaml

# Choose the segmentation model here
# options: unet, resunet, fcn
version:
  {
    minimum: 0.1.6-dev,
    maximum: 0.1.6-dev
  }
model:
  {
    dimension: 3, # the dimension of the model and dataset: defines dimensionality of computations
    base_filters: 32, # Set base filters: number of filters present in the initial module of the U-Net convolution; for IncU-Net, keep this divisible by 4
    architecture: resunet, # options: unet, resunet, fcn, uinc
    final_layer: sigmoid, # can be either sigmoid, softmax or none (none == regression)
    norm_type: instance, # can be either batch or instance
    class_list: [0, 255], # Set the list of labels the model should train on and predict
    amp: False, # Set if you want to use Automatic Mixed Precision for your operations or not - options: True, False
    # n_channels: 3, # set the input channels - useful when reading RGB or images that have vectored pixel types
  }
metrics:
  - dice
  - precision
  - iou
  - f1
  - recall: { average: macro }
problem_type: segmentation_brats
verbose: True
inference_mechanism: { grid_aggregator_overlap: average, patch_overlap: 0 }
modality: rad
# Patch size during training - 2D patch for breast images since third dimension is not patched
patch_size: [32, 32, 32]
# Number of epochs
num_epochs: 1
patience: 1
# Set the batch size
batch_size: 1
# Set the initial learning rate
learning_rate: 0.001
# Set the learning rate scheduler i.e. the way the initial learning rate must be updated while the training progresses
# Options: steplr, exponentiallr, cosineannealinglr, reducelronplateau, cycliclr
scheduler: triangle
# Set which loss function you want to use - options : 'dc' - for dice only, 'dcce' - for sum of dice and CE and you can guess the next (only lower-case please)
# options: dc (dice only), ce (), dcce (sume of dice and ce), mse (), ...
loss_function: dc
weighted_loss: True
# Which optimizer do you want to use - adam/sgd
optimizer: adam
# the value of 'k' for cross-validation, this is the percentage of total training data to use as validation;
# randomized split is performed using sklearn's KFold method
# for single fold run, use '-' before the fold number
nested_training: {
    testing: -5, # this controls the holdout data splits for final model evaluation; use '1' if this is to be disabled
    validation: -5, # this controls the validation data splits for model training
  }
# various data augmentation techniques
# options: affine, elastic, downsample, motion, ghosting, bias, blur, gaussianNoise, swap
# keep/edit as needed
# all transforms: https://torchio.readthedocs.io/transforms/transforms.html?highlight=transforms
data_augmentation: {}
# 'spatial':{
#   'probability': 0.5
# },
# 'kspace':{
#   'probability': 0.5
# },
# 'bias':{
#   'probability': 0.5
# },
# 'blur':{
#   'probability': 0.5
# },
# 'noise':{
#   'probability': 0.5
# },
# 'swap':{
#   'probability': 0.5
# }
data_preprocessing: {
    # 'threshold':{
    #   'min': 10,
    #   'max': 75
    # },
    # 'clip':{
    #   'min': 10,
    #   'max': 75
    # },
    "normalize",
    # 'resample':{
    #   'resolution': [1,2,3]
    # },
    #'resize': [128,128], # this is generally not recommended, as it changes image properties in unexpected ways
  }
# data postprocessing node
data_postprocessing: {}
# 'largest_component',
# 'hole_filling'
# parallel training on HPC - here goes the command to prepend to send to a high performance computing
# cluster for parallel computing during multi-fold training
# not used for single fold training
# this gets passed before the training_loop, so ensure enough memory is provided along with other parameters
# that your HPC would expect
# ${outputDir} will be changed to the outputDir you pass in CLI + '/${fold_number}'
#parallel_compute_command: <insert parallel command here>

q_max_length: 1

q_samples_per_volume: 1

q_num_workers: 0

panoptica_config: !Panoptica_Evaluator
  decision_metric: null
  decision_threshold: null
  edge_case_handler: !EdgeCaseHandler
    empty_list_std: !EdgeCaseResult NAN
    listmetric_zeroTP_handling:
      !Metric DSC: !MetricZeroTPEdgeCaseHandling {empty_prediction_result: !EdgeCaseResult ZERO,
        empty_reference_result: !EdgeCaseResult ZERO, no_instances_result: !EdgeCaseResult NAN,
        normal: !EdgeCaseResult ZERO}
      !Metric NSD: !MetricZeroTPEdgeCaseHandling {empty_prediction_result: !EdgeCaseResult INF,
        empty_reference_result: !EdgeCaseResult INF, no_instances_result: !EdgeCaseResult NAN,
        normal: !EdgeCaseResult INF}
  expected_input: !InputType SEMANTIC
  global_metrics: [!Metric DSC]
  instance_approximator: !ConnectedComponentsInstanceApproximator {cca_backend: null}
  instance_matcher: !NaiveThresholdMatching {allow_many_to_one: false, matching_metric: !Metric IOU,
    matching_threshold: 0.5}
  instance_metrics: [!Metric DSC, !Metric NSD]
  log_times: false
  save_group_times: false
  segmentation_class_groups: !SegmentationClassGroups
    groups:
      snfh: !LabelGroup
        single_instance: false
        value_labels: [2]
      et: !LabelGroup
        single_instance: false
        value_labels: [3]
      netc: !LabelGroup
        single_instance: false
        value_labels: [1]
      rc: !LabelGroup
        single_instance: false
        value_labels: [4]
      tc: !LabelMergeGroup
        single_instance: false
        value_labels: [1, 3, 4]
      wt: !LabelMergeGroup
        single_instance: false
        value_labels: [1, 2, 3, 4]
  verbose: false