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Quadratic autoencoder for low-dose CT denoising
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Paper Abstract

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 autoencoder, and apply it for low-dose CT denoising. Then, we perform experiments on the Mayo low-dose CT dataset to demonstrate that the quadratic autoencoder yields a better denoising performance.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110722Z (28 May 2019); doi: 10.1117/12.2534908
Show Author Affiliations
Fenglei Fan, Rensselaer Polytechnic Institute (United States)
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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