Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network
Gao, Xiaohong W. 
ORCID: https://orcid.org/0000-0002-8103-6624, Wen, Xuesong ORCID: https://orcid.org/0000-0001-6518-8962, Li, Dong ORCID: https://orcid.org/0000-0001-9240-4173, Liu, Weiping, Xiong, Jichun, Xu, Bin, Liu, Juan, Zhang, Heng and Liu, Xuefeng
(2022)
Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network.
Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging.
In: SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, 20-22 Feb 2022, San Diego, USA.
.
ISSN 0277-786X
[Conference or Workshop Item]
(doi:10.1117/12.2624423)
|
PDF
- Final accepted version (with author's formatting)
Download (827kB) | Preview |
Abstract
Human papillomavirus (HPV) remains a leading cause of virus-induced cancers. Hence early detection of HPV plays a crucial role in providing timely, optimal and effective intervention before such a cancer develops. While conventional light microscopy constitutes one of inseparable tools applied for studying biological cell structures, its low resolution at ~100nm per pixel falls short of detecting HPV that typically has a size of 52 to 55nm in diameter, giving rise to visualisation of HPV and subsequent evaluation of the efficacy of anti-HPV drugs at such sub-pixel level a challenging task if not overwhelmingly. This study employs an explainable deep learning network of texture transformer (TTSR) to up sample by four folds (×4). In comparison with other super resolution approaches, TTSR appears to perform the best with PSNR and SSIM being 28.70 and 0.8778 respectively whereas 25.80/0.7910, 18.35/0.5059. 30.31/0.8013, and 28.07/0.6074 are observed for the methods of RCAN, Pix2Pix, CycleGAN, and ESRGAN respectively. Significantly, the training pairs of TTSR does not need to be precisely match between low (LR) and high resolution (HR) images since the LR and HR images, which are required by many other super resolution approaches. This work constitutes one of the first to detect HPV applying explainable deep learning network, which will lead to the real world implementation to evaluate the efficacy of the developed anti-HPV drugs.
| Item Type: | Conference or Workshop Item (Other) |
|---|---|
| Additional Information: | This is a poster presentation and a paper contribution |
| Research Areas: | A. > School of Science and Technology |
| Item ID: | 34585 |
| Notes on copyright: | Copyright 2022 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited
Xiaohong W. Gao, Xuesong Wen, Dong Li, Weiping Liu, Jichun Xiong, Xuefeng Liu, "Detection of human papillomavirus (HPV) from super resolution microscopic images applying a texture transformer network," Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203627 (4 April 2022); https://doi.org/10.1117/12.2624423 |
| Useful Links: | |
| Depositing User: | Xiaohong Gao |
| Date Deposited: | 21 Jan 2022 16:12 |
| Last Modified: | 29 Nov 2022 17:34 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/34585 |
Actions (login required)
 |
View Item |
Statistics
Additional statistics are available via IRStats2.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)