• Journal of Innovative Optical Health Sciences
  • Vol. 15, Issue 4, 2250025 (2022)
[in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]*, and [in Chinese]
Author Affiliations
  • School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, Guangdong, P. R. China
  • show less
    DOI: 10.1142/s1793545822500250 Cite this Article
    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model[J]. Journal of Innovative Optical Health Sciences, 2022, 15(4): 2250025 Copy Citation Text show less
    References

    [1] B. J. Battersby, G. A. Lawrie, M. Trau, "Optical encoding of microbeads for gene screening: Alternatives to microarrays," Drug Discovery Today 6(1) (2001).

    [2] X. J. Chen, L. Xie, Y. He, T. Guan, X. Zhou, B. Wang, G. Feng, H. Yu, Y. Ji, "Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning," Analyst 144(14), 4312–4319 (2019).

    [3] Y. Leng, K. Sun, X. Chen, W. Li, "Suspension arrays based on nanoparticle-encoded microspheres for high-throughput multiplexed detection," Chem. Soc. Rev. 44(15), 5552–5595 (2015).

    [4] P. Su, N. Liu, M. Zhu, B. Ning, M. Liu, Z. Yang, X. Pan, Z. Gao, "Simultaneous detection of five antibiotics in milk by high-throughput suspension array technology," Talanta 85(2), 1160–1165 (2011).

    [5] Y. Wang, B. Ning, Y. Peng, J. Bai, M. Liu, X. Fan, Z. Sun, Z. Lv, C. Zhou, Z. Gao, "Application of suspension array for simultaneous detection of four different mycotoxins in corn and peanut," Biosens. Bioelectron. 41(1), 391–396 (2013).

    [6] R. Wilson, A. R. Cossins, D. G. Spiller, "Encoded microcarriers for high-throughput multiplexed detection," Chemin Form 38(1) (2007).

    [7] Z. Wang, S. Zong, L. Wu, D. Zhu, Y. Cui, SERSactivated platforms for immunoassay: Probes, encoding methods, and applications (2017).

    [8] J. H. Kim, H. Kang, S. Kim, B. H. Jun, T. Kang, J. Chae, S. Jeong, J. Kim, D. H. Jeong, Y. S. Lee, "Encoding peptide sequences with surface-enhanced Raman spectroscopic nanoparticles," Chem. Commun. 47(8), 2306–2308 (2011).

    [9] X. Chen, Q. He, T. Guan, Y. He, G. Feng, B. Wang, B. Lu, Y. Ji, "Dual-digital encoded suspension array based on Raman spectroscopy and laser induced breakdown spectroscopy for multiplexed biodetection," Sensor Actuators B: Chem. (2018).

    [10] E. Moen, D. Bannon, T. Kudo, W. Graf, M. Covert, D. Van Valen, "Deep learning for cellular image analysis," Nature Methods 16(12), 1233–1246 (2019).

    [11] M. Nadif, F. Role, "Unsupervised and self-supervised deep learning approaches for biomedical text mining," Briefings Bioinform. 22(2), 1592–1603 (2021).

    [12] Chen, Xin, J. Weng, W. Luo, W. Lu, H. Wu, J. Xu, Q. Tian, "Sample balancing for deep learning-based visual recognition," IEEE Trans. Neural Netw. Learn. Syst. 31(10), 3962–3976 (2020).

    [13] Z. Q. Zhao, P. Zheng, S. T. Xu, X. Wu, "Object detection with deep learning: A review," IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019).

    [14] V. Sorin, Y. Barash, E. Konen, E. Klang, Deeplearning natural language processing for oncological applications," Lancet Oncol. 21(12), 1553–1556 (2020).

    [15] J. W. Kang, Y. S. Park, H. Chang, W. Lee, S. P. Singh, W. Choi, L. H. Galindo, R. R. Dasari, S. H. Nam, J. Park, P. T. C. So, Direct observation of glucose fingerprint using in vivo Raman spectroscopy, Sci. Adv. 6(4), 2–10 (2020).

    [16] S. Weng, H. Yuan, X. Zhang, P. Li, L. Zheng, J. Zhao, L. Huang, "Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy," Analyst 145(14), 4827–4835 (2020).

    [17] S. Ali, M. Hassan, M. Saleem, S. F. Tahir, "Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images," Int. J. Imag. Syst. Technol. 31(1), 94–105 (2021).

    [18] C. Berghian-Grosan, D. A. Magdas, "Application of Raman spectroscopy and machine learning algorithms for fruit distillates discrimination," Scientific Reports 10(1), 1–9 (2020).

    [19] H. Shin, S. Oh, S. Hong, M. Kang, D. Kang, Y. G. Ji, B. H. Choi, K. W. Kang, H. Jeong, Y. Park, H. K. Kim, Y. Choi, "Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes," ACS Nano 14(5), 5435–5444 (2020).

    [20] W. M. Kouw, M. Loog, "A review of domain adaptation without target labels," IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 766–785 (2021).

    [21] H. Shi, H. Wang, X. Meng et al., "Setting up a surface-enhanced raman scattering database for arti ficial-intelligence-based label-free discrimination of tumor suppressor genes," Anal. Chem. 90(24), 14216–14221 (2018).

    [22] P. Isola, J. Y. Zhu, T. Zhou, A. A. Efros, "Image-toimage translation with conditional adversarial networks," Proc. 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, pp. 5967–5976 (2017).

    [23] J. Y. Zhu, T. Park, P. Isola, A. A. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," Proc. IEEE Int. Conf. Computer Vision, 2017 October, pp. 2242–2251 (2017).

    [24] J. Hoffman, E. Tzeng, T. Park, J. Y. Zhu, P. Isola, K. Saenko, A. A. Efros, T. Darrell, "CyCADA: Cycle-consistent adversarial domain adaptation," 35th International Conference on Machine Learning, ICML 2018, pp. 3162–3174 (2018).

    [25] S. Liu, B. Zhang, Y. Liu, A. Han, H. Shi, T. Guan, Y. He, "Unpaired stain transfer using pathologyconsistent constrained generative adversarial networks," IEEE Trans. Med. Imaging 40(8), 1977–1989 (2021).

    [26] M. Mirza, S. Osindero, Conditional Generative Adversarial Nets (2014).

    [27] K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition," Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2016-December, pp. 770–778 (2016).

    [28] R. Shu, H. H. Bui, H. Narui, S. Ermon, "A DIrt-t approach to unsupervised domain adaptation," 6th international conference on learning representations ICLR 2018 — Conference Track Proceedings, pp. 1–19 (2018).

    [29] T. DeVries, G. W. Taylor, Improved regularization of convolutional neural networks with cutout (2017).

    [30] T. Miyato, S. I. Maeda, M. Koyama, S. Ishii, "Virtual adversarial training: A regularization method for supervised and semi-supervised learning," IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2019).

    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Decoding of Raman spectroscopy-encoded suspension arrays based on the detail constraint cycle domain adaptive model[J]. Journal of Innovative Optical Health Sciences, 2022, 15(4): 2250025
    Download Citation