Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images

Gao, Xiaohong W. ORCID logoORCID: https://orcid.org/0000-0002-8103-6624, Wen, Xuesong ORCID logoORCID: https://orcid.org/0000-0001-6518-8962, Li, Dong ORCID logoORCID: https://orcid.org/0000-0001-9240-4173, Liu, Weiping, Xiong, Jichun, Xu, Bin, Liu, Juan, Zhang, Heng and Liu, Xuefeng (2021) Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images. 2021 20th IEEE International Conference On Machine Learning And Applications (ICMLA). In: 20th IEEE ICMLA 2021, 13-16 Dec 2021, Virtual online. e-ISBN 9781665443371, pbk-ISBN 9781665443388. [Conference or Workshop Item] (doi:10.1109/ICMLA52953.2021.00137)

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Abstract

Human papillomavirus (HPV) remains a leading cause of virus-induced cancers and has a typical size of 52 to 55nm in diameter. Hence conventional light microscopy that usually sustains a resolution at ~100nm per pixel falls short of detecting it. This study explores four state of the art generative adversarial networks (GANs) for visualising HPV. The evaluation is achieved by counting the HPV clusters that are corrected identified as well as drug treated cultured cells, i.e. no HPVs. The average sensitivity and specificity are 78.81%, 76.37%, 76.62% and 84.71% for CycleGAN, Pix2pix, ESRGAN and Pix2pixHD respectively. For ESRGAN, the training takes place by matching pairs between low and high resolution (x4) images. For the other three networks, the translation is performed from original raw images to their coloured maps that have undertaken Gaussian filtering in order to discern HPV clusters visually. Pix2pixHD appears to perform the best.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 34138
Notes on copyright: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Xiaohong Gao
Date Deposited: 18 Nov 2021 17:43
Last Modified: 07 Sep 2022 14:20
URI: https://eprints.mdx.ac.uk/id/eprint/34138

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