Breast Histopathology Image Segmentation Part 3

Guangzhou, China

Github

Model Creation - ResNet50
Model Creation - Custom CNN Model
- Construction

Based on Breast Histopathology Images by Paul Mooney. Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. Can recurring breast cancer be spotted with AI tech? - BBC News

Citation: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Dataset: 198,738 IDC(negative) image patches; 78,786 IDC(positive) image patches

Model Creation - ResNet50

Start by loading the pre-trained model:

./train_ResNet50_32_20k.py

# Loading the ResNet50, ensuring the head Full Connected layers are left off / removed
baseModel = ResNet50(weights="imagenet", include_top=False, input_tensor=Input(shape=(48, 48, 3)))

Construct the head of the model that will be placed on top of the the base model:

## Flattening is converting the data into a 1-dimensional array for inputting it to the next layer. 
## We flatten the output of the convolutional layers to create a single long feature vector. 
### Average pooling computes the average of the elements present in the region of feature map covered by the filter.
#### ReLU stands for Rectified Linear Unit. 
#### The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time.
## Dropout is a technique where randomly selected neurons are ignored during training.

headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7), padding="same")(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(256, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(len(config.CLASSES), activation="softmax")(headModel)

# Placing the head model on top of the base model
model = Model(inputs=baseModel.input, outputs=headModel)

# Loop over all the layers of the base model and freeze them so that they are 
# not updated during the training process
for layer in baseModel.layers:
    layer.trainable = False

Model Creation - Custom CNN Model

Construction

Instead of downloading a pre-trained model we can build our own. The layers of the Custom Model are constructed according to:

utils/conv_bc_model.py

class BC_Model:
    @staticmethod
    def build(width, height, depth, classes):
    
        # Lets first initialize the model with input shape to be "channels last" and channel's dimension
        model = Sequential()
        inputShape = (height, width, depth)
        chanDim = -1
        
        # If we are using "channels first", then let's update the input shape and channel's dimension
        if K.image_data_format() == "channels_first":
            inputShape = (depth, height, width)
            chanDim = 1
            
        # (CONV2D => RELU => BatchNormalization ) * 1 => POOL => DROPOUT
        model.add(Conv2D(32, (3, 3), padding="same", input_shape=inputShape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))
        
        # (CONV2D => RELU => BatchNormalization ) * 2 => POOL => DROPOUT
        model.add(Conv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))
        
        # (CONV2D => RELU => BatchNormalization ) * 3 => POOL => DROPOUT
        model.add(Conv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))
        
        # (CONV2D => RELU => BatchNormalization ) * 4 => POOL => DROPOUT
        model.add(Conv2D(256, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(256, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(256, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(Conv2D(256, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=chanDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))
        
        # FC => RELU layers => BatchNormalization => DROPOUT
        model.add(Flatten())
        model.add(Dense(512))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))
        
        # Dense layer and softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))
        
        # Returning the created network architecture
        return model

We can use this function to build the model:

./train_CustomModel_32_conv_20k.py

# Building the model
print("Building the model")
model = BC_Model.build(width=48, height=48, depth=3, classes=2)

Model Creation - ResNet50​

Model Creation - Custom CNN Model​

Construction​

Model Creation - ResNet50

Model Creation - Custom CNN Model

Construction