Machine Learning in Electromagnetics With Applications to Biomedical Imaging: A Review

被引：51

作者：

Li, Maokun ^{[1
]}

Guo, Rui ^{[1
]}

Zhang, Ke ^{[1
]}

Lin, Zhichao ^{[1
]}

Yang, Fan ^{[1
]}

Xu, Shenheng ^{[1
]}

Chen, Xudong ^{[2
]}

Massa, Andrea ^{[3
,4
,5
]}

Abubakar, Aria ^{[6
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100086, Peoples R China

[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore

[3] Univ Trento, I-38123 Trento, Italy

[4] Univ Elect Sci & Technol China, Chengdu 610097, Peoples R China

[5] Tsinghua Univ, Beijing 100086, Peoples R China

[6] Schlumberger, Data Sci Digital Subsurface Solut, Houston, TX 77056 USA

来源：

IEEE ANTENNAS AND PROPAGATION MAGAZINE | 2021年 / 63卷 / 03期

基金：

国家重点研发计划; 美国国家科学基金会;

关键词：

Imaging; Machine learning; Biomedical imaging; Biomedical measurement; Training; Machine learning algorithms; Physics; LOW-DOSE CT; NEURAL-NETWORK; NOISE-REDUCTION; RECONSTRUCTION; CLASSIFICATION; COMBINATION; REMOVAL; DOMAIN;

D O I：

10.1109/MAP.2020.3043469

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：

Biomedical imaging is a relevant noninvasive technique aimed at generating an image of the biological structure under analysis. The arising visual representation of the characteristics of the object is affected by both the measurement process and reconstruction algorithm. This procedure can be considered as a hybridization of data information, measurement physics, and prior information.

引用

页码：39 / 51

页数：13

共 120 条

[41]

Goodfellow I., 2014, PROC NEURAL INF PRO, P2680

[42] Supervised Descent Learning Technique for 2-D Microwave Imaging [J].

Guo, Rui ;

Song, Xiaoqian ;

Li, Maokun ;

Yang, Fan ;

Xu, Shenheng ;

Abubakar, Aria .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2019, 67 (05) :3550-3554

[43]

Habashy TM, 2004, PROG EL RES, V46, P265, DOI 10.2528/PIER03100702

[44] Imaging pathologic pulmonary air and fluid accumulation by functional and absolute EIT [J].

Hahn, G. ;

Just, A. ;

Dudykevych, T. ;

Frerichs, I. ;

Hinz, J. ;

Quintel, M. ;

Hellige, G. .

PHYSIOLOGICAL MEASUREMENT, 2006, 27 (05) :S187-S198

[45] Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks [J].

Hamilton, S. J. ;

Hauptmann, A. .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (10) :2367-2377

[46] Learning a variational network for reconstruction of accelerated MRI data [J].

Hammernik, Kerstin ;

Klatzer, Teresa ;

Kobler, Erich ;

Recht, Michael P. ;

Sodickson, Daniel K. ;

Pock, Thomas ;

Knoll, Florian .

MAGNETIC RESONANCE IN MEDICINE, 2018, 79 (06) :3055-3071

[47]

Hampe N., 2019, arXiv:1908.04118

[48] Deep learning with domain adaptation for accelerated projection-reconstruction MR [J].

Han, Yoseob ;

Yoo, Jaejun ;

Kim, Hak Hee ;

Shin, Hee Jung ;

Sung, Kyunghyun ;

Ye, Jong Chul .

MAGNETIC RESONANCE IN MEDICINE, 2018, 80 (03) :1189-1205

[49]

Hasan M. A., 2009, Journal of Software Engineering and Applications, V2, P330, DOI 10.4236/jsea.2009.25043

[50] UNIVERSAL APPROXIMATION OF AN UNKNOWN MAPPING AND ITS DERIVATIVES USING MULTILAYER FEEDFORWARD NETWORKS [J].

HORNIK, K ;

STINCHCOMBE, M ;

WHITE, H .

NEURAL NETWORKS, 1990, 3 (05) :551-560

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