residual unet

### Residual U-Net Architecture in Deep Learning for Image Segmentation

Residual connections, introduced to address the degradation problem observed when training very deep networks[^4], have been successfully integrated into various architectures including U-Net. The combination of residual blocks with U-Net leverages both high-resolution spatial information and low-level feature maps while mitigating vanishing gradient issues.

In a **Residual U-Net**, each encoding block not only downsamples features but also includes skip connections that bypass one or more layers. These shortcuts allow gradients to flow through an alternative route during backpropagation, facilitating deeper network training without compromising performance:

def res_block(x, filters):
    shortcut = x
    
    conv1 = tf.keras.layers.Conv2D(filters=filters,
                                  kernel_size=(3, 3),
                                  padding='same',
                                  activation='relu')(x)
    
    conv2 = tf.keras.layers.Conv2D(filters=filters,
                                  kernel_size=(3, 3),
                                  padding='same')(conv1)

    output = tf.keras.layers.Add()([shortcut, conv2])
    return tf.keras.activations.relu(output)


def build_res_unet(input_shape, num_classes=2):
    inputs = tf.keras.Input(shape=input_shape)

    # Encoder path (with residual blocks)
    c1 = res_block(inputs, 64)
    p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)

    c2 = res_block(p1, 128)
    p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)

    # Bottleneck
    b = res_block(p2, 256)

    # Decoder path (concatenation from encoder paths)
    u7 = tf.keras.layers.UpSampling2D((2, 2), interpolation="bilinear")(b)
    cat_7 = tf.keras.layers.Concatenate(axis=-1)([u7, c2])

    d7 = res_block(cat_7, 128)

    u8 = tf.keras.layers.UpSampling2D((2, 2), interpolation="bilinear")(d7)
    cat_8 = tf.keras.layers.Concatenate(axis=-1)([u8, c1])

    d8 = res_block(cat_8, 64)

    outputs = tf.keras.layers.Conv2D(num_classes, (1, 1), activation='softmax')(d8)

    model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
    return model

The integration of residuals within U-Net enhances its capability by allowing direct propagation of identity mappings alongside learned transformations at different scales. This design choice significantly improves convergence speed and final accuracy on complex tasks such as medical image segmentation[^2].