Embedding Deep Learning in Inverse Scattering Problems

Sanghvi, Yash; Kalepu, Yaswanth; Khankhoje, Uday K.

doi:10.1109/tci.2019.2915580

Cited by 111 publications

(47 citation statements)

References 41 publications

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“…An interesting extension of the method described will be to the analysis of the attraction of parabolic pulses towards a selfsimilar state in normally dispersive nonlinear fibers with linear gain [4]. Furthermore, although demonstrated here in a fiber optics context, the principle of using NN architectures to solve wave equation-based inverse problems is expected to apply to many physical systems [30,31].…”

Section: Discussionmentioning

confidence: 99%

Artificial neural networks for nonlinear pulse shaping in optical fibers

Boscolo

Finot

2020

Optics & Laser Technology

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We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.

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Section: Discussionmentioning

confidence: 99%

Artificial neural networks for nonlinear pulse shaping in optical fibers

Boscolo

Finot

2020

Optics & Laser Technology

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“…CNN's with only the last layer having a connected structure can work much faster than traditional neural networks and reduce the computational burden faced in inverse scattering problems. Some of the latest works have already reported the various deep learning paradigms to help simplify the complex inverse problem [210][211][212][213]. Moreover, Micrima and EMTensor teams have already engaged in improvising their clinical models using deep learning.…”

Section: Open Challenges Faced By Mwi and Future Prospectsmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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Microwave imaging (MWI) is a non-ionizing, non-invasive and an upcoming affordable medical imaging modality. Over the last few decades, MWI has invited active research towards biomedical imaging, with special focus on breast tumor detection. After long years of intense research and clinical trials, a breast tumour monitoring unit based on MWI is finally entering clinical imaging scenarios. In this manuscript, the vast literature in MWI to date has been consolidated, and an indetail study of the state-of-the-art for breast tumor detection has been presented. The hurdles faced during clinical trials are discussed, and their possible solutions and future directions for a fast transition into clinical imaging have been presented. It is hoped that this paper can serve as a guide for MWI researchers and practitioners, especially those new to the field to comprehend the potential of MWI as a viable imaging tool for breast imaging.

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“…The predicted dielectric images by the CNN are then used as the starting image for the microwave inverse scattering imaging as a physics-based image refinement step. In [23], a CNN, named CS-Net, is first proposed to be trained to predict a good estimate of the total contrast source, i.e., the J(r) in Eqs. (3) and (4).…”

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

203

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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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Embedding Deep Learning in Inverse Scattering Problems

Cited by 111 publications

References 41 publications

Artificial neural networks for nonlinear pulse shaping in optical fibers

Artificial neural networks for nonlinear pulse shaping in optical fibers

An Overview of Microwave Imaging for Breast Tumor Detection

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

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