Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Invited review: Valorization of dairy permeates-Balancing value creation with commercial feasibility.

Journal of dairy science·2026

Same author

Comparative analysis of meat quality of Laiwu black, Minxinan black and Hyla rabbits.

Archives animal breeding·2026

Same author

Stage-dependent roles of Trichoderma longibrachiatum and manganese sulfate in humification and nitrogen conservation during chicken manure composting.

Bioresource technology·2026

Same author

Habitat-informed MRI Radiomics and Deep Learning Fusion for Short-Term Survival Prediction in Patients with Glioblastoma: Exploratory Radiogenomic and Immune Correlates.

Academic radiology·2026

Same author

Investigating the phytohormone-antioxidant interplay mediated by soil amendments and <i>Trichoderma harzianum</i> for enhanced cadmium remediation in <i>Cosmos bipinnatus</i> and <i>Amorpha fruticosa</i>.

International journal of phytoremediation·2026

Same author

Artificial Intelligence Music Platform for Piano Majors Improves Learning and Reduces Anxiety: Randomized Study with Heart Rate Variability Cues.

Journal of visualized experiments : JoVE·2026

Same journal

Optimal Series Resistors for On-Wafer Calibrations.

IEEE transactions on microwave theory and techniques·2026

Same journal

System-Level Calibration of a Millimeter-Wave Vector Signal Analyzer with Uncertainties.

IEEE transactions on microwave theory and techniques·2026

Same journal

Glass Microwave Microfluidic Devices for Broadband Characterization of Diverse Fluids.

IEEE transactions on microwave theory and techniques·2025

Same journal

A Wireless Artificial Mechanoreceptor in 180-nm CMOS.

IEEE transactions on microwave theory and techniques·2024

Same journal

An Improved Two-Port Transmission Line Permittivity and Permeability Determination Method With Shorted Sample.

IEEE transactions on microwave theory and techniques·2024

Same journal

Jitter Sensitivity Analysis of the Superconducting Josephson Arbitrary Waveform Synthesizer.

IEEE transactions on microwave theory and techniques·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

Near-Field Microwave Scattering Formulation by A Deep Learning Method.

Wenyi Shao¹, Beibei Zhou²

¹Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287 USA.

IEEE Transactions on Microwave Theory and Techniques

|June 14, 2023

Summary

This summary is machine-generated.

Deep learning models electromagnetic scattering for microwave breast imaging, achieving speeds 10,000 times faster than traditional methods. This advancement shows deep learning

Keywords:

Computational electromagnetics convolutional neural network deep learning microwave imaging

More Related Videos

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jul 26, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Computational Electromagnetics
Artificial Intelligence

Background:

Microwave breast imaging (MBI) offers a promising alternative for breast cancer detection.
Accurate electromagnetic (EM) scattering modeling is crucial for MBI but computationally intensive.
Current methods like the method of moments (MOM) face significant computational challenges.

Purpose of the Study:

To develop and validate a deep learning (DL) approach for accelerating EM scattering computations in MBI.
To assess the impact of DL-derived scattering data on MBI image reconstruction accuracy.
To evaluate the speed enhancement offered by DL compared to traditional EM solvers.

Main Methods:

A neural network (NN) was designed to model EM scattering, accepting 2D dielectric breast maps at 3 GHz.
The NN was trained using 18,000 synthetic digital breast phantoms and pre-calculated scattered-field data from the method of moments (MOM).
Generative adversarial networks (GANs) were used for phantom generation, and validation involved comparing NN and MOM data on 2,000 independent datasets.

Main Results:

The NN accurately predicted scattered-field data, with errors that did not significantly impact image reconstruction quality.
The DL-based EM scattering computation achieved a speed improvement of nearly 10,000 times compared to MOM.
Image reconstruction using NN-generated data yielded comparable results to those obtained with MOM data.

Conclusions:

Deep learning provides a highly efficient method for EM scattering computation in MBI.
The developed NN model demonstrates the potential of AI to overcome computational bottlenecks in MBI.
DL offers a viable and significantly faster alternative for EM scattering calculations, paving the way for real-time MBI applications.