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breastnet.py
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breastnet.py
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import tensorflow.keras.backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Activation, Add, BatchNormalization, Concatenate, Conv2D, Dense, \
Dropout, GlobalAveragePooling2D, GlobalMaxPooling2D, Input, Lambda, \
LeakyReLU, MaxPooling2D, Multiply, Permute, Reshape, UpSampling2D \
def cbam_block(cbam_feature, ratio=8):
# Author: @kobiso (https://github.com/kobiso)
"""Contains the implementation of Convolutional Block Attention Module(CBAM) block.
As described in https://arxiv.org/abs/1807.06521.
"""
cbam_feature = channel_attention(cbam_feature, ratio)
cbam_feature = spatial_attention(cbam_feature)
return cbam_feature
def channel_attention(input_feature, ratio=8):
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
channel = input_feature.shape[channel_axis]
shared_layer_one = Dense(channel//ratio,
activation='relu',
kernel_initializer='he_normal',
use_bias=True,
bias_initializer='zeros')
shared_layer_two = Dense(channel,
kernel_initializer='he_normal',
use_bias=True,
bias_initializer='zeros')
avg_pool = GlobalAveragePooling2D()(input_feature)
avg_pool = Reshape((1,1,channel))(avg_pool)
assert avg_pool.shape[1:] == (1,1,channel)
avg_pool = shared_layer_one(avg_pool)
assert avg_pool.shape[1:] == (1,1,channel//ratio)
avg_pool = shared_layer_two(avg_pool)
assert avg_pool.shape[1:] == (1,1,channel)
max_pool = GlobalMaxPooling2D()(input_feature)
max_pool = Reshape((1,1,channel))(max_pool)
assert max_pool.shape[1:] == (1,1,channel)
max_pool = shared_layer_one(max_pool)
assert max_pool.shape[1:] == (1,1,channel//ratio)
max_pool = shared_layer_two(max_pool)
assert max_pool.shape[1:] == (1,1,channel)
cbam_feature = Add()([avg_pool,max_pool])
cbam_feature = Activation('sigmoid')(cbam_feature)
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return Multiply()([input_feature, cbam_feature])
def spatial_attention(input_feature):
kernel_size = 7
if K.image_data_format() == "channels_first":
channel = input_feature.shape[1]
cbam_feature = Permute((2,3,1))(input_feature)
else:
channel = input_feature.shape[-1]
cbam_feature = input_feature
avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
assert avg_pool.shape[-1] == 1
max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
assert max_pool.shape[-1] == 1
concat = Concatenate(axis=3)([avg_pool, max_pool])
assert concat.shape[-1] == 2
cbam_feature = Conv2D(filters = 1,
kernel_size=kernel_size,
strides=1,
padding='same',
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(concat)
assert cbam_feature.shape[-1] == 1
if K.image_data_format() == "channels_first":
cbam_feature = Permute((3, 1, 2))(cbam_feature)
return Multiply()([input_feature, cbam_feature])
def residual_block(y, nb_channels, _strides=(1, 1), _project_shortcut=False):
# Author: @mjdietzx (https://gist.github.com/mjdietzx)
shortcut = y
y = Conv2D(nb_channels, kernel_size=(3, 3), strides=_strides, padding='same')(y)
y = BatchNormalization()(y)
y = LeakyReLU()(y)
y = Conv2D(nb_channels, kernel_size=(3, 3), strides=(1, 1), padding='same')(y)
y = BatchNormalization()(y)
if _project_shortcut or _strides != (1, 1):
shortcut = Conv2D(nb_channels, kernel_size=(1, 1), strides=_strides, padding='same')(shortcut)
shortcut = BatchNormalization()(shortcut)
y = Add()([shortcut, y])
y = LeakyReLU()(y)
return y
def BreastNet(input_shape=(224,224,3), n_classes=4):
"""
M. Togaçar, K.B. Özkurt, B. Ergen et al., BreastNet: A novel ˘
convolutional neural network model through histopathological images for the diagnosis of breast
cancer, Physica A (2019), doi: https://doi.org/10.1016/j.physa.2019.123592 .
"""
dropRate = 0.3
init = Input(input_shape)
x = Conv2D(32, (3, 3), activation=None, padding='same')(init)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(32, (3, 3), activation=None, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x1 = MaxPooling2D((2,2))(x)
x = Conv2D(64, (3, 3), activation=None, padding='same')(x1)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = cbam_block(x)
x = residual_block(x, 64)
x2 = MaxPooling2D((2,2))(x)
x = Conv2D(128, (3, 3), activation=None, padding='same')(x2)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = cbam_block(x)
x = residual_block(x, 128)
x3 = MaxPooling2D((2,2))(x)
ginp1 = UpSampling2D(size=(2, 2), interpolation='bilinear')(x1)
ginp2 = UpSampling2D(size=(4, 4), interpolation='bilinear')(x2)
ginp3 = UpSampling2D(size=(8, 8), interpolation='bilinear')(x3)
hypercolumn = Concatenate()([ginp1, ginp2, ginp3])
gap = GlobalAveragePooling2D()(hypercolumn)
x = Dense(256, activation=None)(gap)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(dropRate)(x)
x = Dense(256, activation=None)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
y = Dense(n_classes, activation="softmax", name="BreastNet")(x)
model = Model(init, y)
return model