Upload configs/base_config.yaml

configs/base_config.yaml

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+# MSLesSeg Framework Base Configuration
+# Multiple Sclerosis Lesion Segmentation Framework
+
+project:
+  name: "MSLesSeg_Segmentation"
+  seed: 42
+  experiment_dir: "./experiments"
+  checkpoint_dir: "./checkpoints"
+
+dataset:
+  name: "MSLesSeg"
+  data_dir: "./data/MSLesSeg"
+  modalities: ["FLAIR", "T1", "T2"]  # Channel order
+  num_modalities: 3
+  num_classes: 2  # background + lesion
+  in_channels: 3
+  out_channels: 2
+  
+  # Preprocessing
+  spacing: [1.0, 1.0, 1.0]  # Target isotropic spacing in mm
+  orientation: "RAS"
+  normalize: "zscore_nonzero"  # zscore_nonzero, zscore, minmax
+  crop_foreground: true
+  
+  # Patch-based training
+  patch_size: [128, 128, 128]
+  patch_overlap: 0.5
+  
+  # Splits
+  split_strategy: "patient_wise"  # patient_wise, stratified, random
+  val_ratio: 0.15
+  test_ratio: 0.0  # Official test set held out
+  num_folds: 5
+  fold_id: 0
+  
+  # Class imbalance handling
+  pos_neg_ratio: 1.0  # For RandCropByPosNegLabeld
+  num_samples_per_volume: 4
+
+data_augmentation:
+  enabled: true
+  
+  spatial:
+    prob_flip: 0.5
+    prob_rotate90: 0.5
+    prob_scale: 0.1
+    scale_range: [0.9, 1.1]
+    prob_shift: 0.5
+    shift_range: [-0.1, 0.1]
+    prob_rotate: 0.2
+    rotate_range: [-0.0524, 0.0524]  # +/- 3 degrees
+    
+  intensity:
+    prob_scale_intensity: 0.1
+    scale_factors: 0.1
+    prob_shift_intensity: 0.5
+    shift_offsets: 0.1
+    prob_gamma: 0.3
+    gamma_range: [0.7, 1.5]
+    prob_gaussian_noise: 0.1
+    noise_std: 0.01
+    
+  elastic:
+    enabled: false  # Memory intensive for 3D
+    prob: 0.2
+    sigma_range: [5, 8]
+    magnitude_range: [1, 2]
+
+model:
+  # Architecture selection
+  name: "SwinUNETR"  # UNet, AttentionUNet, UNetPlusPlus, SwinUNETR, UNETR, SegResNet, VNet, nnUNet
+  
+  # UNet / AttentionUNet / UNetPlusPlus
+  channels: [32, 64, 128, 256, 512]
+  strides: [2, 2, 2, 2]
+  num_res_units: 2
+  
+  # SwinUNETR
+  feature_size: 48
+  use_checkpoint: true  # Gradient checkpointing for memory
+  
+  # UNETR
+  hidden_size: 768
+  mlp_dim: 3072
+  num_heads: 12
+  pos_embed: "conv"
+  
+  # SegResNet
+  init_filters: 32
+  blocks_down: [1, 2, 2, 4]
+  blocks_up: [1, 1, 1]
+  
+  # VNet
+  vnet_channels: [16, 32, 64, 128, 256]
+
+training:
+  batch_size: 2
+  num_epochs: 800
+  num_workers: 4
+  
+  optimizer:
+    name: "AdamW"  # AdamW, SGD, Lion, SAM
+    lr: 0.0001
+    weight_decay: 0.0001
+    
+  scheduler:
+    name: "cosine_warmup"  # cosine_warmup, cosine, onecycle, polynomial, plateau
+    warmup_epochs: 10
+    T_0: 50
+    T_mult: 1
+    min_lr: 1.0e-7
+    
+  loss:
+    name: "DiceCELoss"  # DiceLoss, DiceCELoss, DiceFocalLoss, TverskyLoss, FocalLoss, BoundaryLoss
+    class_weights: null
+    focal_gamma: 2.0
+    tversky_alpha: 0.3
+    tversky_beta: 0.7
+    
+  # Training techniques
+  mixed_precision: true
+  gradient_clip_val: 1.0
+  gradient_accumulation_steps: 1
+  
+  # Validation
+  val_interval: 5
+  save_top_k: 3
+  
+  # Early stopping
+  early_stopping:
+    enabled: true
+    patience: 100
+    monitor: "val_dice"
+    mode: "max"
+
+inference:
+  sliding_window:
+    enabled: true
+    roi_size: [128, 128, 128]
+    sw_batch_size: 4
+    overlap: 0.5
+    mode: "gaussian"
+  
+  test_time_augmentation:
+    enabled: false
+    flips: [[0], [1], [2], [0, 1], [0, 2], [1, 2]]
+    
+  post_processing:
+    apply_sigmoid: false
+    threshold: 0.5
+    remove_small_objects: true
+    min_lesion_size: 3  # mm^3
+    fill_holes: false
+
+metrics:
+  primary: "dice"
+  compute:
+    - dice
+    - iou
+    - precision
+    - recall
+    - sensitivity
+    - specificity
+    - f1
+    - hausdorff_distance
+    - hausdorff_95
+    - volume_similarity
+    - boundary_f1
+    - small_lesion_f1
+    - false_positive_rate
+    - false_discovery_rate
+
+explainability:
+  enabled: true
+  methods:
+    - grad_cam
+    - attention_maps
+    - saliency_maps
+    - uncertainty_monte_carlo
+  mc_samples: 10
+
+ensemble:
+  enabled: false
+  models:
+    - "fold_0_swinunetr"
+    - "fold_1_swinunetr"
+    - "fold_2_swinunetr"
+  method: "average"  # average, weighted, stacking
+  weights: null
+
+tracking:
+  enabled: true
+  backend: "trackio"  # trackio, wandb, tensorboard
+  project_name: "mslesseg-segmentation"
+  experiment_name: "baseline_swinunetr"
+  log_every_n_steps: 10
+
+hyperparameter_optimization:
+  enabled: false
+  backend: "optuna"
+  n_trials: 50
+  search_space:
+    lr: [1.0e-5, 1.0e-3]
+    batch_size: [1, 4]
+    patch_size: [[96, 96, 96], [128, 128, 128]]
+    feature_size: [24, 48, 96]
+    loss: ["DiceCELoss", "DiceFocalLoss"]
+    optimizer: ["AdamW", "SGD"]
+
+# System configuration
+system:
+  gpu_ids: [0]
+  precision: "16-mixed"  # 16-mixed, 32, bf16-mixed
+  deterministic: false
+  benchmark: true