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# Suggestions to Improve BraTS U-Net Segmentation Pipeline
# 1. Enhanced Data Augmentation
from albumentations import Compose, RandomCrop, ElasticTransform, GridDistortion, OpticalDistortion, RandomBrightnessContrast, GaussianNoise, Flip
from sklearn.svm._liblinear import train
def get_augmentation_pipeline():
return Compose([
Flip(p=0.5),
RandomCrop(height=128, width=128, p=0.5),
ElasticTransform(alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03, p=0.5),
GridDistortion(p=0.5),
OpticalDistortion(p=0.5),
GaussianNoise(p=0.5),
RandomBrightnessContrast(p=0.5)
])
augmentation_pipeline = get_augmentation_pipeline()
# Apply this pipeline to your dataset loader as part of preprocessing.
# 2. Switching to Attention U-Net / UNet++ with Pre-trained Encoders
import segmentation_models_pytorch as smp
# Define a UNet++ with a ResNet34 encoder pre-trained on ImageNet
model = smp.UnetPlusPlus(
encoder_name="resnet34", # Encoder architecture
encoder_weights="imagenet", # Use ImageNet pre-trained weights
in_channels=4, # Number of input channels (BraTS has 4 modalities)
classes=4 # Number of output classes
)
# 3. Improved Loss Function
import torch
import torch.nn as nn
from segmentation_models_pytorch.losses import TverskyLoss
# Combine Dice Loss and Tversky Loss
class CombinedLoss(nn.Module):
def __init__(self, alpha=0.5):
super(CombinedLoss, self).__init__()
self.dice_loss = smp.losses.DiceLoss("softmax")
self.tversky_loss = TverskyLoss("softmax", alpha=0.7, beta=0.3)
self.alpha = alpha
def forward(self, y_pred, y_true):
return self.alpha * self.dice_loss(y_pred, y_true) + (1 - self.alpha) * self.tversky_loss(y_pred, y_true)
loss_fn = CombinedLoss()
# 4. Learning Rate Scheduling
from torch.optim.lr_scheduler import CosineAnnealingLR
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
scheduler = CosineAnnealingLR(optimizer, T_max=10, eta_min=1e-5) # Cosine Annealing
# Update the scheduler in each epoch
for epoch in range(num_epochs):
train(...) # Train your model for one epoch
scheduler.step()
# 5. Post-Processing with CRF
import pydensecrf.densecrf as dcrf
def apply_crf(prob_map, img):
d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], 4) # 4 is the number of classes
U = -np.log(prob_map)
d.setUnaryEnergy(U)
# Add pairwise terms
d.addPairwiseGaussian(sxy=3, compat=3)
d.addPairwiseBilateral(sxy=30, srgb=13, rgbim=img, compat=10)
Q = d.inference(5) # Number of iterations
return np.argmax(Q, axis=0).reshape((img.shape[0], img.shape[1]))
# Apply this on your predicted probabilities
# 6. Cross-Validation
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
for train_idx, valid_idx in kf.split(dataset):
train_data = Subset(dataset, train_idx)
valid_data = Subset(dataset, valid_idx)
train_loader = DataLoader(train_data, batch_size=16, shuffle=True)
valid_loader = DataLoader(valid_data, batch_size=16, shuffle=False)
train_model(train_loader, valid_loader)
# 7. Ensemble Learning
class EnsembleModel(nn.Module):
def __init__(self, models):
super(EnsembleModel, self).__init__()
self.models = nn.ModuleList(models)
def forward(self, x):
outputs = [model(x) for model in self.models]
return torch.mean(torch.stack(outputs), dim=0)
# Combine multiple trained models
models = [model1, model2, model3] # Pre-trained models
ensemble_model = EnsembleModel(models)
# 8. Hyperparameter Tuning with Grid Search (Example)
from sklearn.model_selection import ParameterGrid
param_grid = {
'learning_rate': [1e-3, 1e-4],
'batch_size': [8, 16],
'loss_alpha': [0.5, 0.7]
}
for params in ParameterGrid(param_grid):
optimizer = torch.optim.Adam(model.parameters(), lr=params['learning_rate'])
loss_fn = CombinedLoss(alpha=params['loss_alpha'])
train_loader = DataLoader(train_data, batch_size=params['batch_size'])
train_model(train_loader, valid_loader)
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