Supervised Descent Learning Technique for 2-D Microwave Imaging

Guo, Rui; Song, Xiaoqian; Li, Maokun; Yang, Fan; Xu, Shenheng; Abubakar, Aria

doi:10.1109/tap.2019.2902667

Cited by 108 publications

(20 citation statements)

References 13 publications

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“…SDM is widely applied to face alignment [37]. Present work shows its feasibility in microwave imaging [38][39][40]. SDM is able to reconstruct structures accurately in data sparse case by recovering coefficients of compactly supported radial basis functions, which is shown in [41].…”

Section: Introductionmentioning

confidence: 82%

A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

Zhang

Yang

et al. 2021

Electronics

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In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis.

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Section: Introductionmentioning

confidence: 82%

A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

Zhang

Yang

et al. 2021

Electronics

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“…The supervised descent method (SDM) is applied in [20] to tackle microwave imaging, which updates the inversion models using the descent directions collected from the training stage. The whole inversion process is divided into two stages: offline training and online prediction.…”

Section: Learning-assisted Objective-function Approachmentioning

confidence: 99%

A Review of Deep Learning Approaches for Inverse Scattering Problems (Invited Review)

Chen¹,

Wei²,

Li³

et al. 2020

PIER

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205

102

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In recent years, deep learning (DL) is becoming an increasingly important tool for solving inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of deep learning as applied to ISPs. More specifically, we review several state-of-the-art methods of solving ISPs with DL, and we also offer some insights on how to combine neural networks with the knowledge of the underlying physics as well as traditional non-learning techniques. Despite the successes, DL also has its own challenges and limitations in solving ISPs. These fundamental questions are discussed, and possible suitable future research directions and countermeasures will be suggested.

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“…More recently, deep learning has demonstrated promise for solving and/or improving solutions to the inverse scattering problem [24][25][26]. Machine learning has also been shown to successfully improve the quality of MRI and ultrasound medical images, e.g., [27].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

et al. 2021

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A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

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Supervised Descent Learning Technique for 2-D Microwave Imaging

Cited by 108 publications

References 13 publications

A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

A Review of Deep Learning Approaches for Inverse Scattering Problems (Invited Review)

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

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