Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLuo, Shaoboen_US
dc.contributor.authorShi, Yuzhien_US
dc.contributor.authorChin, Lip Keten_US
dc.contributor.authorZhang, Yien_US
dc.contributor.authorWen, Bihanen_US
dc.contributor.authorSun, Yingen_US
dc.contributor.authorNguyen, Binh T. T.en_US
dc.contributor.authorChierchia, Giovannien_US
dc.contributor.authorTalbot, Huguesen_US
dc.contributor.authorBourouina, Tariken_US
dc.contributor.authorJiang, Xudongen_US
dc.contributor.authorLiu, Ai-Qunen_US
dc.identifier.citationLuo, S., Shi, Y., Chin, L. K., Zhang, Y., Wen, B., Sun, Y., Nguyen, B. T. T., Chierchia, G., Talbot, H., Bourouina, T., Jiang, X. & Liu, A. (2021). Rare bioparticle detection via deep metric learning. RSC Advances, 11(29), 17603-17610.
dc.description.abstractRecent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate, e.g., rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalanced, and the input images in inference time could be different from the training images. Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning function maps from image pixels to a latent space, playing a vital role in rare object detection. In this paper, we propose a robust model based on a deep metric neural network for rare bioparticle (Cryptosporidium or Giardia) detection in drinking water. Experimental results showed that the deep metric neural network achieved a high accuracy of 99.86% in classification, 98.89% in precision rate, 99.16% in recall rate and zero false alarm rate. The reported model empowers imaging flow cytometry with capabilities of biomedical diagnosis, environmental monitoring, and other biosensing applications.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofRSC Advancesen_US
dc.rights© 2021 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleRare bioparticle detection via deep metric learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsBiomedical Engineeringen_US
dc.description.acknowledgementThis work was supported by the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13- 2014-01), Ministry of Education Tier 1 RG39/19, and the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001).en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles
Files in This Item:
File Description SizeFormat 
d1ra02869c.pdf1.4 MBAdobe PDFThumbnail

Citations 50

Updated on Mar 24, 2023

Web of ScienceTM
Citations 50

Updated on Mar 23, 2023

Page view(s)

Updated on Mar 24, 2023

Download(s) 50

Updated on Mar 24, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.