def jia_xi_net(images,
training,
nlabels,
scope_name='classifier',
scope_reuse=False):
with tf.variable_scope(scope_name) as scope:
if scope_reuse:
scope.reuse_variables()
conv1_1 = layers.conv3D_layer(images, 'conv1_1', num_filters=32)
pool1 = layers.max_pool_layer3d(conv1_1)
conv2_1 = layers.conv3D_layer(pool1, 'conv2_1', num_filters=64)
pool2 = layers.max_pool_layer3d(conv2_1)
conv3_1 = layers.conv3D_layer(pool2, 'conv3_1', num_filters=128)
conv3_2 = layers.conv3D_layer(conv3_1, 'conv3_2', num_filters=128)
pool3 = layers.max_pool_layer3d(conv3_2)
conv4_1 = layers.conv3D_layer(pool3, 'conv4_1', num_filters=256)
conv4_2 = layers.conv3D_layer(conv4_1, 'conv4_2', num_filters=256)
pool4 = layers.max_pool_layer3d(conv4_2)
dense1 = layers.dense_layer(pool4, 'dense1', hidden_units=512)
dense2 = layers.dense_layer(dense1,
'dense2',
hidden_units=nlabels,
activation=tf.identity)
return dense2
def jia_xi_net_multitask_ordinal(images,
training,
nlabels,
n_age_thresholds=5,
scope_name='classifier',
scope_reuse=False):
with tf.variable_scope(scope_name) as scope:
if scope_reuse:
scope.reuse_variables()
conv1_1 = layers.conv3D_layer(images, 'conv1_1', num_filters=32)
pool1 = layers.max_pool_layer3d(conv1_1)
conv2_1 = layers.conv3D_layer(pool1, 'conv2_1', num_filters=64)
pool2 = layers.max_pool_layer3d(conv2_1)
conv3_1 = layers.conv3D_layer(pool2, 'conv3_1', num_filters=128)
conv3_2 = layers.conv3D_layer(conv3_1, 'conv3_2', num_filters=128)
pool3 = layers.max_pool_layer3d(conv3_2)
conv4_1 = layers.conv3D_layer(pool3, 'conv4_1', num_filters=256)
conv4_2 = layers.conv3D_layer(conv4_1, 'conv4_2', num_filters=256)
pool4 = layers.max_pool_layer3d(conv4_2)
dense1 = layers.dense_layer(pool4, 'dense1', hidden_units=512)
diagnosis = layers.dense_layer(dense1,
'dense2',
hidden_units=nlabels,
activation=tf.identity)
dense_ages = layers.dense_layer(pool4, 'dense_ages', hidden_units=512)
ages_logits = []
for ii in range(n_age_thresholds):
ages_logits.append(
layers.dense_layer(dense_ages,
'age_%s' % str(ii),
hidden_units=2,
activation=tf.identity))
return diagnosis, ages_logits
def unet3D_bn_modified(images, training, nlabels):
images_padded = tf.pad(images, [[0, 0], [44, 44], [44, 44], [16, 16], [0, 0]], 'CONSTANT')
conv1_1 = layers.conv3D_layer_bn(images_padded, 'conv1_1', num_filters=32, kernel_size=(3,3,3), training=training, padding='VALID')
conv1_2 = layers.conv3D_layer_bn(conv1_1, 'conv1_2', num_filters=64, kernel_size=(3,3,3), training=training, padding='VALID')
pool1 = layers.max_pool_layer3d(conv1_2, kernel_size=(2,2,1), strides=(2,2,1))
conv2_1 = layers.conv3D_layer_bn(pool1, 'conv2_1', num_filters=64, kernel_size=(3,3,3), training=training, padding='VALID')
conv2_2 = layers.conv3D_layer_bn(conv2_1, 'conv2_2', num_filters=128, kernel_size=(3,3,3), training=training, padding='VALID')
pool2 = layers.max_pool_layer3d(conv2_2, kernel_size=(2,2,1), strides=(2,2,1))
conv3_1 = layers.conv3D_layer_bn(pool2, 'conv3_1', num_filters=128, kernel_size=(3,3,3), training=training, padding='VALID')
conv3_2 = layers.conv3D_layer_bn(conv3_1, 'conv3_2', num_filters=256, kernel_size=(3,3,3), training=training, padding='VALID')
pool3 = layers.max_pool_layer3d(conv3_2, kernel_size=(2,2,2), strides=(2,2,2))
conv4_1 = layers.conv3D_layer_bn(pool3, 'conv4_1', num_filters=256, kernel_size=(3,3,3), training=training, padding='VALID')
conv4_2 = layers.conv3D_layer_bn(conv4_1, 'conv4_2', num_filters=512, kernel_size=(3,3,3), training=training, padding='VALID')
upconv3 = layers.deconv3D_layer_bn(conv4_2, name='upconv3', kernel_size=(4, 4, 4), strides=(2, 2, 2), num_filters=512, training=training)
concat3 = layers.crop_and_concat_layer([upconv3, conv3_2], axis=4)
conv5_1 = layers.conv3D_layer_bn(concat3, 'conv5_1', num_filters=256, kernel_size=(3,3,3), training=training, padding='VALID')
conv5_2 = layers.conv3D_layer_bn(conv5_1, 'conv5_2', num_filters=256, kernel_size=(3,3,3), training=training, padding='VALID')
upconv2 = layers.deconv3D_layer_bn(conv5_2, name='upconv2', kernel_size=(4, 4, 2), strides=(2, 2, 1), num_filters=256, training=training)
concat2 = layers.crop_and_concat_layer([upconv2, conv2_2], axis=4)
conv6_1 = layers.conv3D_layer_bn(concat2, 'conv6_1', num_filters=128, kernel_size=(3,3,3), training=training, padding='VALID')
conv6_2 = layers.conv3D_layer_bn(conv6_1, 'conv6_2', num_filters=128, kernel_size=(3,3,3), training=training, padding='VALID')
upconv1 = layers.deconv3D_layer_bn(conv6_2, name='upconv1', kernel_size=(4, 4, 2), strides=(2, 2, 1), num_filters=128, training=training)
concat1 = layers.crop_and_concat_layer([upconv1, conv1_2], axis=4)
conv8_1 = layers.conv3D_layer_bn(concat1, 'conv8_1', num_filters=64, kernel_size=(3,3,3), training=training, padding='VALID')
conv8_2 = layers.conv3D_layer_bn(conv8_1, 'conv8_2', num_filters=64, kernel_size=(3,3,3), training=training, padding='VALID')
pred = layers.conv3D_layer_bn(conv8_2, 'pred', num_filters=nlabels, kernel_size=(1,1,1), activation=tf.identity, training=training, padding='VALID')
return pred
def FCN_multitask_ordinal_bn(images,
training,
nlabels,
n_age_thresholds=5,
bn_momentum=0.99,
scope_name='classifier',
scope_reuse=False):
with tf.variable_scope(scope_name) as scope:
if scope_reuse:
scope.reuse_variables()
conv1_1 = layers.conv3D_layer_bn(images,
'conv1_1',
num_filters=32,
training=training,
bn_momentum=bn_momentum)
pool1 = layers.max_pool_layer3d(conv1_1)
conv2_1 = layers.conv3D_layer_bn(pool1,
'conv2_1',
num_filters=64,
training=training,
bn_momentum=bn_momentum)
pool2 = layers.max_pool_layer3d(conv2_1)
conv3_1 = layers.conv3D_layer_bn(pool2,
'conv3_1',
num_filters=128,
training=training,
bn_momentum=bn_momentum)
conv3_2 = layers.conv3D_layer_bn(conv3_1,
'conv3_2',
num_filters=128,
training=training,
bn_momentum=bn_momentum)
pool3 = layers.max_pool_layer3d(conv3_2)
conv4_1 = layers.conv3D_layer_bn(pool3,
'conv4_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
conv4_2 = layers.conv3D_layer_bn(conv4_1,
'conv4_2',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
pool4 = layers.max_pool_layer3d(conv4_2)
conv5_1 = layers.conv3D_layer_bn(pool4,
'conv5_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
conv5_2 = layers.conv3D_layer_bn(conv5_1,
'conv5_2',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
convD_1 = layers.conv3D_layer_bn(conv5_2,
'convD_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
convD_2 = layers.conv3D_layer_bn(convD_1,
'convD_2',
num_filters=nlabels,
training=training,
bn_momentum=bn_momentum,
kernel_size=(1, 1, 1),
activation=tf.identity)
diag_logits = layers.reduce_avg_layer3D(convD_2, name='diagnosis_avg')
convA_1 = layers.conv3D_layer_bn(pool4,
'convA_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
ages_logits = []
for ii in range(n_age_thresholds):
age_activations = layers.conv3D_layer_bn(convA_1,
'convA_2_%d' % ii,
num_filters=2,
training=training,
bn_momentum=bn_momentum,
kernel_size=(1, 1, 1),
activation=tf.identity)
ages_logits.append(
layers.reduce_avg_layer3D(age_activations,
name='age_avg_%d' % ii))
return diag_logits, ages_logits
def FCN_disc_bn(images,
training,
nlabels,
bn_momentum=0.99,
scope_name='discriminator',
scope_reuse=False):
with tf.variable_scope(scope_name) as scope:
if scope_reuse:
scope.reuse_variables()
conv1_1 = layers.conv3D_layer_bn(images,
'conv1_1',
num_filters=32,
training=training,
bn_momentum=bn_momentum)
pool1 = layers.max_pool_layer3d(conv1_1)
conv2_1 = layers.conv3D_layer_bn(pool1,
'conv2_1',
num_filters=64,
training=training,
bn_momentum=bn_momentum)
pool2 = layers.max_pool_layer3d(conv2_1)
conv3_1 = layers.conv3D_layer_bn(pool2,
'conv3_1',
num_filters=128,
training=training,
bn_momentum=bn_momentum)
conv3_2 = layers.conv3D_layer_bn(conv3_1,
'conv3_2',
num_filters=128,
training=training,
bn_momentum=bn_momentum)
pool3 = layers.max_pool_layer3d(conv3_2)
conv4_1 = layers.conv3D_layer_bn(pool3,
'conv4_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
conv4_2 = layers.conv3D_layer_bn(conv4_1,
'conv4_2',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
pool4 = layers.max_pool_layer3d(conv4_2)
conv5_1 = layers.conv3D_layer_bn(pool4,
'conv5_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
conv5_2 = layers.conv3D_layer_bn(conv5_1,
'conv5_2',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
convD_1 = layers.conv3D_layer_bn(conv5_2,
'convD_1',
num_filters=256,
training=training,
bn_momentum=bn_momentum)
convD_2 = layers.conv3D_layer_bn(convD_1,
'convD_2',
num_filters=nlabels,
training=training,
bn_momentum=bn_momentum,
kernel_size=(1, 1, 1),
activation=tf.identity)
diag_logits = layers.reduce_avg_layer3D(convD_2, name='diagnosis_avg')
return diag_logits
def pool_fc_discriminator_bs2(x,
training,
scope_name='discriminator',
scope_reuse=False):
with tf.variable_scope(scope_name) as scope:
if scope_reuse:
scope.reuse_variables()
conv1_1 = layers.conv3D_layer(x,
'dconv1_1',
kernel_size=(3, 3, 3),
num_filters=8,
strides=(1, 1, 1),
activation=layers.leaky_relu)
pool1 = layers.max_pool_layer3d(conv1_1)
conv2_1 = layers.conv3D_layer(pool1,
'dconv2_1',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
pool2 = layers.max_pool_layer3d(conv2_1)
conv3_1 = layers.conv3D_layer(pool2,
'dconv3_1',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
conv3_2 = layers.conv3D_layer_bn(conv3_1,
'dconv3_2',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu,
training=training)
pool3 = layers.max_pool_layer3d(conv3_2)
conv4_1 = layers.conv3D_layer(pool3,
'dconv4_1',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
conv4_2 = layers.conv3D_layer(conv4_1,
'dconv4_2',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
pool4 = layers.max_pool_layer3d(conv4_2)
conv5_1 = layers.conv3D_layer(pool4,
'dconv5_1',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
conv5_2 = layers.conv3D_layer(conv5_1,
'dconv5_2',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
pool5 = layers.max_pool_layer3d(conv5_2)
conv6_1 = layers.conv3D_layer(pool5,
'dconv6_1',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
conv6_2 = layers.conv3D_layer(conv6_1,
'dconv6_2',
kernel_size=(3, 3, 3),
num_filters=16,
strides=(1, 1, 1),
activation=layers.leaky_relu)
pool6 = layers.max_pool_layer3d(conv6_2)
dense1 = layers.dense_layer(pool6,
'ddense1',
hidden_units=256,
activation=layers.leaky_relu)
dense2 = layers.dense_layer(dense1,
'ddense2',
hidden_units=1,
activation=tf.identity)
return dense2