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U-Net Segmentation Models with Attention Mechanisms for Brain Tumor Detection
"""
import tensorflow as tf
from tensorflow.keras import layers, Model
import numpy as np
def convolution_block(input_tensor, num_filters, kernel_size=3, dropout_rate=0.2, use_batchnorm=True):
"""
Double convolution block for U-Net
Args:
input_tensor: Input tensor
num_filters: Number of filters in convolution layers
kernel_size: Kernel size for convolutions
dropout_rate: Dropout rate for regularization
use_batchnorm: Whether to use batch normalization
Returns:
Output tensor after two convolution operations
"""
x = layers.Conv2D(
num_filters,
kernel_size,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(input_tensor)
if use_batchnorm:
x = layers.BatchNormalization()(x)
x = layers.Dropout(dropout_rate)(x)
x = layers.Conv2D(
num_filters,
kernel_size,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(x)
if use_batchnorm:
x = layers.BatchNormalization()(x)
return x
def attention_gate(input_tensor, gating_tensor, num_filters):
"""
Attention gate mechanism for U-Net skip connections
Args:
input_tensor: Feature map from encoder (skip connection)
gating_tensor: Gating signal from decoder (upsampled path)
num_filters: Number of filters
Returns:
Attended feature map
"""
# Interpolate gating tensor to match input_tensor spatial dimensions
gating_tensor = layers.Resizing(
height=input_tensor.shape[1],
width=input_tensor.shape[2],
interpolation='bilinear'
)(gating_tensor)
# Attention mechanism
x1 = layers.Conv2D(num_filters, 1, padding='same', use_bias=False)(input_tensor)
x2 = layers.Conv2D(num_filters, 1, padding='same', use_bias=False)(gating_tensor)
x = layers.Add()([x1, x2])
x = layers.Activation('relu')(x)
x = layers.Conv2D(1, 1, padding='same', use_bias=False)(x)
x = layers.Activation('sigmoid')(x)
# Apply attention weights
attended_tensor = layers.Multiply()([input_tensor, x])
return attended_tensor
def encoder_block(input_tensor, num_filters, dropout_rate=0.2, use_batchnorm=True):
"""
Encoder block: convolution + max pooling
Args:
input_tensor: Input tensor
num_filters: Number of filters
dropout_rate: Dropout rate
use_batchnorm: Whether to use batch normalization
Returns:
Encoded tensor and skip connection
"""
x = convolution_block(input_tensor, num_filters, dropout_rate=dropout_rate, use_batchnorm=use_batchnorm)
p = layers.MaxPooling2D(pool_size=(2, 2))(x)
return x, p
def decoder_block(input_tensor, skip_tensor, num_filters, use_attention=False, dropout_rate=0.2, use_batchnorm=True):
"""
Decoder block: upsampling + concatenation with skip connection + convolution
Args:
input_tensor: Input tensor from previous decoder block
skip_tensor: Skip connection from encoder
num_filters: Number of filters
use_attention: Whether to use attention gate on skip connection
dropout_rate: Dropout rate
use_batchnorm: Whether to use batch normalization
Returns:
Decoded tensor
"""
# Upsampling
x = layers.Conv2DTranspose(
num_filters,
(2, 2),
strides=(2, 2),
padding='same'
)(input_tensor)
# Apply attention gate if enabled
if use_attention:
skip_tensor = attention_gate(skip_tensor, x, num_filters)
# Concatenate with skip connection
x = layers.Concatenate()([x, skip_tensor])
# Convolution block
x = convolution_block(x, num_filters, dropout_rate=dropout_rate, use_batchnorm=use_batchnorm)
return x
def build_unet(
input_shape=(224, 224, 3),
num_classes=1,
base_filters=64,
dropout_rate=0.2,
use_batchnorm=True,
use_attention=False
):
"""
Build U-Net model for brain tumor segmentation
Args:
input_shape: Input image shape (height, width, channels)
num_classes: Number of output classes (1 for binary segmentation)
base_filters: Number of filters in first encoder block
dropout_rate: Dropout rate for regularization
use_batchnorm: Whether to use batch normalization
use_attention: Whether to use attention gates in skip connections
Returns:
U-Net model
"""
inputs = layers.Input(shape=input_shape, name='image_input')
# Normalize input
x = layers.Rescaling(1.0 / 255)(inputs)
# Encoder (downsampling path)
filters = base_filters
skip_connections = []
for i in range(4):
s, x = encoder_block(x, filters, dropout_rate=dropout_rate, use_batchnorm=use_batchnorm)
skip_connections.append(s)
filters *= 2
# Bottleneck
x = convolution_block(x, filters, dropout_rate=dropout_rate, use_batchnorm=use_batchnorm)
# Decoder (upsampling path)
filters //= 2
for i in range(4):
skip = skip_connections.pop()
x = decoder_block(
x,
skip,
filters,
use_attention=use_attention,
dropout_rate=dropout_rate,
use_batchnorm=use_batchnorm
)
filters //= 2
# Output layer
if num_classes == 1:
outputs = layers.Conv2D(num_classes, (1, 1), activation='sigmoid', padding='same')(x)
else:
outputs = layers.Conv2D(num_classes, (1, 1), activation='softmax', padding='same')(x)
model = Model(inputs=[inputs], outputs=[outputs], name='unet')
return model
def build_attention_unet(input_shape=(224, 224, 3), num_classes=1, base_filters=64, dropout_rate=0.2):
"""
Build Attention U-Net model (U-Net with attention gates)
Args:
input_shape: Input image shape
num_classes: Number of output classes
base_filters: Number of filters in first encoder block
dropout_rate: Dropout rate
Returns:
Attention U-Net model
"""
return build_unet(
input_shape=input_shape,
num_classes=num_classes,
base_filters=base_filters,
dropout_rate=dropout_rate,
use_attention=True
)
def build_res_unet(
input_shape=(224, 224, 3),
num_classes=1,
base_filters=64,
dropout_rate=0.2
):
"""
Build Residual U-Net model (U-Net with residual connections)
Args:
input_shape: Input image shape
num_classes: Number of output classes
base_filters: Number of filters in first encoder block
dropout_rate: Dropout rate
Returns:
Residual U-Net model
"""
inputs = layers.Input(shape=input_shape, name='image_input')
# Normalize input
x = layers.Rescaling(1.0 / 255)(inputs)
# Initial convolution
x = layers.Conv2D(base_filters, 3, padding='same', kernel_initializer='he_normal')(x)
# Encoder with residual connections
filters = base_filters
skip_connections = []
for i in range(4):
residual = layers.Conv2D(
filters, 1, padding='same', kernel_initializer='he_normal'
)(x)
x = convolution_block(x, filters, dropout_rate=dropout_rate)
# Add residual connection
x = layers.Add()([x, residual])
skip_connections.append(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
filters *= 2
# Bottleneck
x = convolution_block(x, filters, dropout_rate=dropout_rate)
# Decoder
filters //= 2
for i in range(4):
x = layers.Conv2DTranspose(
filters,
(2, 2),
strides=(2, 2),
padding='same'
)(x)
skip = skip_connections.pop()
x = layers.Concatenate()([x, skip])
x = convolution_block(x, filters, dropout_rate=dropout_rate)
filters //= 2
# Output layer
if num_classes == 1:
outputs = layers.Conv2D(num_classes, (1, 1), activation='sigmoid', padding='same')(x)
else:
outputs = layers.Conv2D(num_classes, (1, 1), activation='softmax', padding='same')(x)
model = Model(inputs=[inputs], outputs=[outputs], name='res_unet')
return model
def build_multi_modal_unet(
input_shapes=[(224, 224, 3), (224, 224, 3)],
num_classes=1,
base_filters=64,
dropout_rate=0.2,
fusion_method='attention'
):
"""
Build multi-modal U-Net with attention fusion for combining multiple input modalities
Args:
input_shapes: List of input shapes for different modalities
num_classes: Number of output classes
base_filters: Number of filters in first encoder block
dropout_rate: Dropout rate
fusion_method: Method for fusing modalities ('attention', 'concat', 'add')
Returns:
Multi-modal U-Net model
"""
inputs = [layers.Input(shape=shape, name=f'modality_{i}_input') for i, shape in enumerate(input_shapes)]
# Process each modality through initial convolutions
modality_features = []
for i, inp in enumerate(inputs):
x = layers.Rescaling(1.0 / 255)(inp)
x = layers.Conv2D(base_filters, 3, padding='same', kernel_initializer='he_normal')(x)
modality_features.append(x)
# Fuse modalities
if fusion_method == 'attention':
# Attention-based fusion
fused = attention_fusion(modality_features, base_filters)
elif fusion_method == 'concat':
fused = layers.Concatenate()(modality_features)
fused = layers.Conv2D(base_filters, 1, padding='same')(fused)
elif fusion_method == 'add':
fused = layers.Add()(modality_features)
else:
raise ValueError(f"Unknown fusion method: {fusion_method}")
# Continue with U-Net architecture
filters = base_filters
skip_connections = []
# Encoder
for i in range(4):
s, fused = encoder_block(fused, filters, dropout_rate=dropout_rate)
skip_connections.append(s)
filters *= 2
# Bottleneck
fused = convolution_block(fused, filters, dropout_rate=dropout_rate)
# Decoder
filters //= 2
for i in range(4):
skip = skip_connections.pop()
fused = decoder_block(fused, skip, filters, dropout_rate=dropout_rate)
filters //= 2
# Output layer
if num_classes == 1:
outputs = layers.Conv2D(num_classes, (1, 1), activation='sigmoid', padding='same')(fused)
else:
outputs = layers.Conv2D(num_classes, (1, 1), activation='softmax', padding='same')(fused)
model = Model(inputs=inputs, outputs=outputs, name='multi_modal_unet')
return model
def attention_fusion(feature_maps, num_filters):
"""
Attention-based fusion of multiple feature maps
Args:
feature_maps: List of feature maps to fuse
num_filters: Number of filters for attention computation
Returns:
Fused feature map
"""
if len(feature_maps) == 1:
return feature_maps[0]
# Compute attention weights for each feature map
attention_weights = []
for fm in feature_maps:
# Global average pooling to get channel-wise statistics
gap = layers.GlobalAveragePooling2D()(fm)
# Learn attention weights
w = layers.Dense(num_filters, activation='relu')(gap)
w = layers.Dense(num_filters, activation='softmax')(w)
w = layers.Reshape((1, 1, num_filters))(w)
attention_weights.append(w)
# Apply attention weights and sum
weighted_features = [layers.Multiply()([fm, w]) for fm, w in zip(feature_maps, attention_weights)]
fused = layers.Add()(weighted_features)
return fused
def dice_coefficient(y_true, y_pred, smooth=1e-6):
"""
Dice coefficient metric for segmentation evaluation
Args:
y_true: Ground truth masks
y_pred: Predicted masks
smooth: Smoothing factor to avoid division by zero
Returns:
Dice coefficient value
"""
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred, smooth=1e-6):
"""
Dice loss function for training segmentation models
Args:
y_true: Ground truth masks
y_pred: Predicted masks
smooth: Smoothing factor
Returns:
Dice loss value
"""
return 1 - dice_coefficient(y_true, y_pred, smooth)
def combined_loss(weights=None):
"""
Combined loss function (weighted sum of dice loss and binary crossentropy)
Args:
weights: Weights for each loss component [dice_weight, bce_weight]
Returns:
Combined loss function
"""
if weights is None:
weights = [0.5, 0.5]
def loss(y_true, y_pred):
bce = tf.keras.losses.binary_crossentropy(y_true, y_pred)
dice = dice_loss(y_true, y_pred)
return weights[0] * dice + weights[1] * bce
return loss
def iou_metric(y_true, y_pred, smooth=1e-6):
"""
Intersection over Union (IoU) metric for segmentation
Args:
y_true: Ground truth masks
y_pred: Predicted masks
smooth: Smoothing factor
Returns:
IoU value
"""
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
union = tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) - intersection
return (intersection + smooth) / (union + smooth)
def get_segmentation_model(model_name='unet', **kwargs):
"""
Factory function to get segmentation models
Args:
model_name: Name of the model ('unet', 'attention_unet', 'res_unet', 'multi_modal_unet')
**kwargs: Additional arguments passed to the model builder
Returns:
Segmentation model
"""
models = {
'unet': build_unet,
'attention_unet': build_attention_unet,
'res_unet': build_res_unet,
'multi_modal_unet': build_multi_modal_unet
}
if model_name not in models:
raise ValueError(f"Unknown model name: {model_name}. Available models: {list(models.keys())}")
return models[model_name](**kwargs) |