Quadratic Autoencoder for Low-Dose CT Denoising
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Recently, deep learning has transformed many fields including medical imaging. Inspired by diversity of biological neurons, our group proposed quadratic neurons in which the inner product in current artificial neurons is replaced with a quadratic operation on inputs, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in representative network architectures, towards quadratic neuron based deep learning. In this regard, our prior theoretical studies have shown important merits of quadratic neurons and networks. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred to as the quadratic auto-encoder, and apply it for low-dose CT de-noising. Then, we perform experiments on the Mayo low-dose CT dataset to demonstrate that the quadratic auto-encoder yields a better de-noising performance.
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