Compact Convolutional Neural Networks for Ultrasound Beamforming

被引：0

作者：

Chen, Zhanwen ^{[1
]}

Luchies, Adam ^{[2
]}

Byram, Brett ^{[2
]}

机构：

[1] Vanderbilt Univ, Dept Elect Engn & Comp Sci, 221 Kirkland Hall, Nashville, TN 37235 USA

[2] Vanderbilt Univ, Dept Biomed Engn, 221 Kirkland Hall, Nashville, TN 37235 USA

来源：

2019 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS) | 2019年

关键词：

ultrasound; beamforming; convolutional neural networks; deep learning;

D O I：

10.1109/ultsym.2019.8925949

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We trained convolutional neural networks (CNNs) to suppress off-axis scattering in the short-time Fourier Transform (STFT) domain. Our training data were point target responses from simulated anechoic cysts. We used random neural architecture search to build CNN models with variable input formulations, layer sizes, and training hyperparameters. Our results showed that CNNs were easier to train, as they required fewer network weights to match the performance of fully-connected networks (FCNs). The best CNN models achieved comparable phantom CNRs with with two to three orders of magnitude fewer weights.

引用

页码：560 / 562

页数：3

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