  Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin and
Eosin (H&E) staining is the gold standard for oncology care. H&E staining is
slow and uses dyes, reagents and precious tissue samples that cannot be reused.
Thousands of native nonstained RGB Whole Slide Image (RWSI) patches of prostate
core tissue biopsies were registered with their H&E stained versions.
Conditional Generative Adversarial Neural Networks (cGANs) that automate
conversion of native nonstained RWSI to computational H&E stained images were
then trained. High similarities between computational and H&E dye stained
images with Structural Similarity Index (SSIM) 0.902, Pearsons Correlation
Coefficient (CC) 0.962 and Peak Signal to Noise Ratio (PSNR) 22.821 dB were
calculated. A second cGAN performed accurate computational destaining of H&E
dye stained images back to their native nonstained form with SSIM 0.9, CC 0.963
and PSNR 25.646 dB. A single-blind study computed more than 95% pixel-by-pixel
overlap between prostate tumor annotations on computationally stained images,
provided by five-board certified MD pathologists, with those on H&E dye stained
counterparts. We report the first visualization and explanation of neural
network kernel activation maps during H&E staining and destaining of RGB images
by cGANs. High similarities between kernel activation maps of computational and
H&E stained images (Mean-Squared Errors <0.0005) provide additional
mathematical and mechanistic validation of the staining system. Our neural
network framework thus is automated, explainable and performs high precision
H&E staining and destaining of low cost native RGB images, and is computer
vision and physician authenticated for rapid and accurate tumor diagnoses.
