Detection of human papillomavirus (HPV) from super resolution microscopic images applying a texture transformer network

Xiaohong W. Gao; Xuesong Wen; Dong Li; Weiping Liu; Jichun Xiong; Xuefeng Liu

doi:10.1117/12.2624423

4 April 2022 Detection of human papillomavirus (HPV) from super resolution microscopic images applying a texture transformer network

Xiaohong W. Gao, Xuesong Wen, Dong Li, Weiping Liu, Jichun Xiong, Xuefeng Liu

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203627 (2022) https://doi.org/10.1117/12.2624423
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

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.

Conference Presentation

Citation Download Citation

Xiaohong W. Gao, Xuesong Wen, Dong Li, Weiping Liu, Jichun Xiong, and 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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available