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

[1] Total variation as a multiplicative constraint for solving inverse problems [J].

Abubakar, A ;

van den Berg, PM .

IEEE TRANSACTIONS ON IMAGE PROCESSING, 2001, 10 (09) :1384-1392

[2] BIOMEDICAL IMAGING MODALITIES - A TUTORIAL [J].

ACHARYA, R ;

WASSERMAN, R ;

STEVENS, J ;

HINOJOSA, C .

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 1995, 19 (01) :3-25

[3] Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network [J].

Acharya, U. Rajendra ;

Fujita, Hamido ;

Lih, Oh Shu ;

Hagiwara, Yuki ;

Tan, Jen Hong ;

Adam, Muhammad .

INFORMATION SCIENCES, 2017, 405 :81-90

[4] Learned Primal-Dual Reconstruction [J].

Adler, Jonas ;

Oktem, Ozan .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (06) :1322-1332

[5] Solving ill-posed inverse problems using iterative deep neural networks [J].

Adler, Jonas ;

Oktem, Ozan .

INVERSE PROBLEMS, 2017, 33 (12)

[6] MoDL: Model-Based Deep Learning Architecture for Inverse Problems [J].

Aggarwal, Hemant K. ;

Mani, Merry P. ;

Jacob, Mathews .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (02) :394-405

[7] K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation [J].

Aharon, Michal ;

Elad, Michael ;

Bruckstein, Alfred .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (11) :4311-4322

[8] Identification of EMG signals using discriminant analysis and SVM classifier [J].

Alkan, Ahmet ;

Gunay, Mucahid .

EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (01) :44-47

[9]

Ammari H, 2008, MATH APPL-BERLIN, V62, P1

[10]

[Anonymous], 2017, ARXIV171102074

← 1 2 3 4 5 6 7 8 9 10 →