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Abstract #5163

Implicit Regularization for Improving Phase-based EPT with Stein’s Unbiased Risk Estimator

Chuanjiang Cui1, Kyu-Jin Jung1, Jun-Hyeong Kim1, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of

Synopsis

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue PropertiesPhase-based EPT algorithm is extremely sensitive to noise. Although various denoising algorithms have been introduced to suppress noise amplification, residual artifact cause instability conductivity error or broadening boundary artifact. In this work, we propose a novel generative network trained with Stein’s unbiased risk estimator under the purely unsupervised learning framework, which improve the performance of phase-based conductivity reconstruction algorithms. In addition, the proposed method does not need any dataset for training neural network and not require any prior information for designing explicit regularization.

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