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

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.