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- # Pipline Scenario to run
- # "scen_train_all" - Train and evaluate model, with noise
- # "scen_load_all" - Load and evaluate model, with noise
- # "scen_load_eval" - Load and evaluate model, without noise
- scenario = "scen_train_all"
- [data]
- mri_files_path = "../data/PET_volumes_customtemplate_float32/"
- xls_file_path = "../data/LP_ADNIMERGE.csv"
- seed = 42
- data_splits = [0.7, 0.2, 0.1] # train, validation, test
- image_channels = 1
- clin_data_channels = 2
- num_classes = 2 # AD, NL
- [training]
- device = "cuda:0" # "cpu", "cuda", "mps"
- batch_size = 32
- ensemble_size = 30
- droprate = 0.05
- learning_rate = 0.0001
- num_epochs = 25
- deterministic = false # force deterministic cuDNN kernels (slower, exact reproduction)
- [evaluation]
- # Gaussian noise standard deviations applied to images during noisy evaluation
- # (step 6). 0.0 is the clean baseline and should be kept first.
- noise_levels = [0.0, 0.02, 0.05, 0.1, 0.2]
- # Monte-Carlo forward passes used to estimate Bayesian predictive/model
- # uncertainty (step 5/6). Ignored for the deterministic ensemble.
- mc_passes = 30
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