Quadratic autoencoder for low-dose CT denoising

Fenglei Fan; Hongming Shan; Ge Wang

doi:10.1117/12.2534908

28 May 2019 Quadratic autoencoder for low-dose CT denoising

Fenglei Fan, Hongming Shan, Ge Wang

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110722Z (2019) https://doi.org/10.1117/12.2534908
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

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.

Citation Download Citation

Fenglei Fan, Hongming Shan, and Ge Wang "Quadratic autoencoder for low-dose CT denoising", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110722Z (28 May 2019); https://doi.org/10.1117/12.2534908

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