  Inspired by complexity and diversity of biological neurons, our group
proposed quadratic neurons by replacing the inner product in current artificial
neurons with a quadratic operation on input data, thereby enhancing the
capability of an individual neuron. Along this direction, we are motivated to
evaluate the power of quadratic neurons in popular network architectures,
simulating human-like learning in the form of quadratic-neuron-based deep
learning. Our prior theoretical studies have shown important merits of
quadratic neurons and networks in representation, efficiency, and
interpretability. In this paper, we use quadratic neurons to construct an
encoder-decoder structure, referred as the quadratic autoencoder, and apply it
to low-dose CT denoising. The experimental results on the Mayo low-dose CT
dataset demonstrate the utility of quadratic autoencoder in terms of image
denoising and model efficiency. To our best knowledge, this is the first time
that the deep learning approach is implemented with a new type of neurons and
demonstrates a significant potential in the medical imaging field.
