Radiol. 290(3) 649-656

chen2019.py

"""
Residual U-net implementation based on [1].

Usage:

>>> from chen2019 import network
>>> # input_data.shape == (num_slices, slice_height, slice_width, num_channels)
>>> model = network(input_data.shape[1:])
>>> model.fit(input_data, output_date, epochs=100, batch_size=input_data.shape[0] // 4, ...)

TODO:

- do we need more epochs?
- do we use triplets of convolutional blocks rather than pairs?
- do we use a different batch size?

[1] K. T. Chen et al. 2019 Radiol. 290(3) 649-656
"Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs"
"""
from tensorflow import keras

__author__ = "Casper da Costa-Luis <casper.dcl@kcl.ac.uk>"


def network(input_shape, residual_input_channel=1, lr=2e-4, dtype="float32"):
    """
    residual_input_channel  : input channel index to use for residual addition
    """
    x = inputs = keras.layers.Input(input_shape, dtype=dtype)

    def block(x, filters):
        x = keras.layers.Conv2D(filters, 3, padding="same", use_bias=False, dtype=dtype)(x)
        x = keras.layers.BatchNormalization(dtype=dtype)(x)
        x = keras.layers.ReLU(dtype=dtype)(x)
        return x

    # U-net
    filters = [16, 32, 64, 128]
    ## encode
    convs = []
    for i in filters[:-1]:
        x = block(x, i)
        x = block(x, i)
        # TODO: do we need a third block?
        convs.append(x)
        x = keras.layers.MaxPool2D(dtype=dtype, padding="same")(x)
    x = block(x, filters[-1])
    x = block(x, filters[-1])
    ## decode
    for i in filters[:-1][::-1]:
        x = keras.layers.UpSampling2D(interpolation="bilinear", dtype=dtype)(x)
        x = keras.layers.Concatenate()([x, convs.pop()])
        x = block(x, i)
        x = block(x, i)
    x = keras.layers.Conv2D(1, 1, padding="same", dtype=dtype, name="residual")(x)
    x = keras.layers.Add(name="generated")(
        [inputs[..., residual_input_channel : residual_input_channel + 1], x]
    )

    model = keras.Model(inputs=inputs, outputs=x)

    opt = keras.optimizers.Adam(lr)
    model.compile(opt, loss="mae")
    model.summary()
    return model

Incorporated into NiftyML (https://www.nifty.ml/en/latest/examples/)