def createOutputNode(self, inputs):
xlist = inputs[0]
xsize = K.int_shape(xlist)
output_shape = (-1, xsize[2], xsize[3], xsize[4], xsize[5])
xlist = Lambda(self.postprocessTensor,
output_shape=output_shape)(xlist, output_shape)
coordlist = inputs[1]
corrdsize = K.int_shape(coordlist)
output_shape = (-1, corrdsize[2], corrdsize[3], corrdsize[4],
corrdsize[5])
coordlist = Lambda(self.postprocessTensor,
output_shape=output_shape)(coordlist, output_shape)
noduleFeat, nodulePred = self.NoduleNet(xlist, coordlist)
output_shape = (corrdsize[0], corrdsize[1], -1)
nodulePred = Lambda(self.postprocessTensor,
output_shape=output_shape)(nodulePred,
output_shape)
featshape = K.int_shape(noduleFeat)
centerFeat = MaxPooling3D()(pool_size=2, strides=2, padding="valid")(
noduleFeat[:, :, featshape[2] / 2 - 1:featshape[2] / 2 + 1,
featshape[3] / 2 - 1:featshape[3] / 2 + 1,
featshape[4] / 2 - 1:featshape[4] / 2 + 1])
centerFeat = centerFeat[:, :, 0, 0, 0]
out = Dropout(0.5)(centerFeat)
fc1 = Dense(64, name="fc1")(out)
fc1_active = Activation(K.relu)(fc1)
fc2 = Dense(1, name="fc2")(fc1_active)
fc2_active = Activation(K.sigmoid)(fc2)
output_shape = (xsize[0], xsize[1])
out = Lambda(self.postprocessTensor,
output_shape=output_shape)(fc2_active, output_shape)
base_prob = tf.sigmoid(tf.constant(-30.0, dtype=tf.float32))
casePred = 1 - K.prod(1 - out,
axis=1) * (1 - base_prob.expand(out.size()[0]))
return nodulePred, casePred, out
