config.yaml

# Config file for the experiment

# experiment_name: "real_only_prevalence_0.25_balanced"
experiment_name: "synth_only"

params:
  max_epochs: 10
  batch_size: 64
  model: "resnet18"
  # model: "densenet121"
  # model: "resnet3D"
  # model: "resnet(2+1)D"
  # model: "swin3d_t"
  # model: "mc3"
  learning_rate: 0.0001
  weight_decay: null
  early_layers_tuning: False
  fine_tuning: False
  gray2rgb: True
  standardize_histograms: False
  mask_images: False

  video: True
  volume: False
  edes: True

  # video: False
  # volume: True

  input_size: 120
  time_step: 4 # skip every nth frame to reduce the number of frames
  central_slice_only: True # if True, only the central slice is used for training # deprecated
  slice_neighbourhood: 1 # will use the neighoburhood of the central slice (both spatially and temporally) to augment the number of training samples

  seed: 42
  n_splits: 5
  fold: 0
  n_repeats: 8
  saved_split: True
  

dataset:
  train_prevalence: 0.25
  val_prevalence: 0.1
  test_prevalence: 0.01

  balance_by_sensitive_features: True

  # icd_code: "I50"
  # icd_code: "C09_HF"
  icd_code: 
   - "C09_HF"
  #  - "C01_IHD"
  #  - "C02_MI"
  #  - "C04_AF"
  #  - "R03_High_cholesterol"
  #  - "R01_Hypertension"

  # icd_code: "HF"
  # view: "la_4ch"
  view: "sa"
  # name: "MnM"
  name: "UKBB"
  load_masks: True
  UKBB:
    # csv file with patient's metadata including sex, bmi, age, and diagnosis
    dataset_file: "data/ukbb_dataset.csv"

  use_real: True # if False, the real data will be used solely for validation and testing
  use_synthetic: False
  synthetic_path: "/path/to/synthetic/dataset/"
  synthetic_samples_to_load: 10000

use_torchio: True

num_workers: 20
cache_data: True
cache_path: "cache/sa_edes/"

# debug: False # if True, only use 10 samples for training and validation
debug: False