A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation

Naike Du; Xiuzhu Ye

doi:10.1109/AP-S/INC-USNC-URSI52054.2024.10686379

A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation

Naike Du, Xiuzhu Ye

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a transformer-based neural network, for segmenting the images of human tissues obtained by inverse scattering method. Firstly, the distribution image of relative permittivity for the human tissue is obtained by the subspace-based optimization method (SOM). Then the obtained results are fed into a transformer-based network to output a segmentation mask. Numerical results verify that this method can get clear edges for different tissues, and it can achieve accurate classification for human tissue imaging.

Original language	English
Title of host publication	2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	251-252
Number of pages	2
ISBN (Electronic)	9798350369908
DOIs	https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10686379
Publication status	Published - 2024
Event	2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Florence, Italy Duration: 14 Jul 2024 → 19 Jul 2024

Publication series

Name	IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
ISSN (Print)	1522-3965

Conference

Conference	2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024
Country/Territory	Italy
City	Florence
Period	14/07/24 → 19/07/24

Keywords

imaging segmentation
inverse scattering method
SOM
transformer-based network

Access to Document

10.1109/AP-S/INC-USNC-URSI52054.2024.10686379

Cite this

Du, N., & Ye, X. (2024). A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation. In 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings (pp. 251-252). (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10686379

Du, Naike ; Ye, Xiuzhu. / A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation. 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 251-252 (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)).

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title = "A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation",

abstract = "This paper proposes a transformer-based neural network, for segmenting the images of human tissues obtained by inverse scattering method. Firstly, the distribution image of relative permittivity for the human tissue is obtained by the subspace-based optimization method (SOM). Then the obtained results are fed into a transformer-based network to output a segmentation mask. Numerical results verify that this method can get clear edges for different tissues, and it can achieve accurate classification for human tissue imaging.",

keywords = "imaging segmentation, inverse scattering method, SOM, transformer-based network",

author = "Naike Du and Xiuzhu Ye",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 ; Conference date: 14-07-2024 Through 19-07-2024",

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language = "English",

series = "IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings",

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Du, N & Ye, X 2024, A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation. in 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings. IEEE Antennas and Propagation Society, AP-S International Symposium (Digest), Institute of Electrical and Electronics Engineers Inc., pp. 251-252, 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024, Florence, Italy, 14/07/24. https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10686379

A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation. / Du, Naike; Ye, Xiuzhu.
2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. p. 251-252 (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Du, Naike

AU - Ye, Xiuzhu

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N2 - This paper proposes a transformer-based neural network, for segmenting the images of human tissues obtained by inverse scattering method. Firstly, the distribution image of relative permittivity for the human tissue is obtained by the subspace-based optimization method (SOM). Then the obtained results are fed into a transformer-based network to output a segmentation mask. Numerical results verify that this method can get clear edges for different tissues, and it can achieve accurate classification for human tissue imaging.

AB - This paper proposes a transformer-based neural network, for segmenting the images of human tissues obtained by inverse scattering method. Firstly, the distribution image of relative permittivity for the human tissue is obtained by the subspace-based optimization method (SOM). Then the obtained results are fed into a transformer-based network to output a segmentation mask. Numerical results verify that this method can get clear edges for different tissues, and it can achieve accurate classification for human tissue imaging.

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Du N, Ye X. A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation. In 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2024. p. 251-252. (IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)). doi: 10.1109/AP-S/INC-USNC-URSI52054.2024.10686379

A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation

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