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

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High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin  Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural  Networks