Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

Gwiyeong Moon; Taehwang Son; Hongki Lee; Donghyun Kim

doi:10.1117/12.2632959

Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

Gwiyeong Moon, Taehwang Son, Hongki Lee, Donghyun Kim

Department of Electrical and Electronic Engineering

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

Abstract

We investigate the method to analyze interferometric plasmonic microscopy (IPM) images using a deep learning approach. An IPM image was generated by employing an optical model: the image intensity was formed by reflected and scattered fields. Convolutional neural network was utilized for the classification of IPM images. Conventional detection method based on fourier filtering was taken for comparison with the proposed method. It was confirmed that deep learning improves the performance significantly, in particular, robustness to noise. These results suggested applicability of deep learning beyond IPM images with higher efficiency.

Original language	English
Title of host publication	Plasmonics
Subtitle of host publication	Design, Materials, Fabrication, Characterization, and Applications XX
Editors	Din Ping Tsai, Takuo Tanaka, Yu-Jung Lu
Publisher	SPIE
ISBN (Electronic)	9781510653788
DOIs	https://doi.org/10.1117/12.2632959
Publication status	Published - 2022
Event	Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX 2022 - San Diego, United States Duration: 2022 Aug 21 → 2022 Aug 25

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12197
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX 2022
Country/Territory	United States
City	San Diego
Period	22/8/21 → 22/8/25

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation (NRF) grants (NRF-2022R1A4A2000748) funded Korean Government and the Korea Medical Device Development Fund (RS-2020-KD000088).

Publisher Copyright:
© 2022 SPIE.

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2632959

Cite this

Moon, G., Son, T., Lee, H., & Kim, D. (2022). Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle. In D. P. Tsai, T. Tanaka, & Y-J. Lu (Eds.), Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX Article 121970B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12197). SPIE. https://doi.org/10.1117/12.2632959

Moon, Gwiyeong ; Son, Taehwang ; Lee, Hongki et al. / Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle. Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX. editor / Din Ping Tsai ; Takuo Tanaka ; Yu-Jung Lu. SPIE, 2022. (Proceedings of SPIE - The International Society for Optical Engineering).

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abstract = "We investigate the method to analyze interferometric plasmonic microscopy (IPM) images using a deep learning approach. An IPM image was generated by employing an optical model: the image intensity was formed by reflected and scattered fields. Convolutional neural network was utilized for the classification of IPM images. Conventional detection method based on fourier filtering was taken for comparison with the proposed method. It was confirmed that deep learning improves the performance significantly, in particular, robustness to noise. These results suggested applicability of deep learning beyond IPM images with higher efficiency.",

author = "Gwiyeong Moon and Taehwang Son and Hongki Lee and Donghyun Kim",

note = "Funding Information: This work was supported by the National Research Foundation (NRF) grants (NRF-2022R1A4A2000748) funded Korean Government and the Korea Medical Device Development Fund (RS-2020-KD000088). Publisher Copyright: {\textcopyright} 2022 SPIE.; Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX 2022 ; Conference date: 21-08-2022 Through 25-08-2022",

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doi = "10.1117/12.2632959",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

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editor = "Tsai, {Din Ping} and Takuo Tanaka and Yu-Jung Lu",

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Moon, G, Son, T, Lee, H & Kim, D 2022, Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle. in DP Tsai, T Tanaka & Y-J Lu (eds), Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX., 121970B, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12197, SPIE, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX 2022, San Diego, United States, 22/8/21. https://doi.org/10.1117/12.2632959

Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle. / Moon, Gwiyeong; Son, Taehwang; Lee, Hongki et al.
Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX. ed. / Din Ping Tsai; Takuo Tanaka; Yu-Jung Lu. SPIE, 2022. 121970B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12197).

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

TY - GEN

T1 - Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

AU - Moon, Gwiyeong

AU - Son, Taehwang

AU - Lee, Hongki

AU - Kim, Donghyun

N1 - Funding Information: This work was supported by the National Research Foundation (NRF) grants (NRF-2022R1A4A2000748) funded Korean Government and the Korea Medical Device Development Fund (RS-2020-KD000088). Publisher Copyright: © 2022 SPIE.

PY - 2022

Y1 - 2022

N2 - We investigate the method to analyze interferometric plasmonic microscopy (IPM) images using a deep learning approach. An IPM image was generated by employing an optical model: the image intensity was formed by reflected and scattered fields. Convolutional neural network was utilized for the classification of IPM images. Conventional detection method based on fourier filtering was taken for comparison with the proposed method. It was confirmed that deep learning improves the performance significantly, in particular, robustness to noise. These results suggested applicability of deep learning beyond IPM images with higher efficiency.

AB - We investigate the method to analyze interferometric plasmonic microscopy (IPM) images using a deep learning approach. An IPM image was generated by employing an optical model: the image intensity was formed by reflected and scattered fields. Convolutional neural network was utilized for the classification of IPM images. Conventional detection method based on fourier filtering was taken for comparison with the proposed method. It was confirmed that deep learning improves the performance significantly, in particular, robustness to noise. These results suggested applicability of deep learning beyond IPM images with higher efficiency.

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M3 - Conference contribution

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BT - Plasmonics

A2 - Tsai, Din Ping

A2 - Tanaka, Takuo

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PB - SPIE

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Moon G, Son T, Lee H, Kim D. Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle. In Tsai DP, Tanaka T, Lu Y-J, editors, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX. SPIE. 2022. 121970B. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2632959

Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

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