tests.py

# 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)