Dielectric Breast Phantom by A Conditional GAN

Wenyi Shao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Synthetic dielectric breast phantoms are generated by conditional generative adversarial network (CGAN) in this paper. Phantoms produced by the generative neural network are 128 by 128 pixels for frequency 3 GHz. The generated phantoms can be used in electromagnetic simulations for microwave breast imaging (MBI) research and can serve as the training data to develop machine learning algorithms for MBI research.

Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages343-344
Number of pages2
ISBN (Electronic)9781665496582
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States
Duration: Jul 10 2022Jul 15 2022

Publication series

Name2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/TerritoryUnited States
CityDenver
Period7/10/227/15/22

Keywords

  • CGAN
  • deep learning
  • microwave breast imaging

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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