Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising

Understand

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.