  Histopathology tissue samples are widely available in two states:
paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB
images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains
in histology but suffers from several shortcomings related to tissue
preparation, staining protocols, slowness and human error. We report two novel
approaches for training machine learning models for the computational H&E
staining and destaining of prostate core biopsy RGB images. The staining model
uses a conditional generative adversarial network that learns hierarchical
non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core
biopsy before and after H&E staining. The trained staining model can then
generate computationally H&E-stained prostate core WSRIs using previously
unseen non-stained biopsy images as input. The destaining model, by learning
mappings between an H&E stained WSRI and a non-stained WSRI of the same biopsy,
can computationally destain previously unseen H&E-stained images. Structural
and anatomical details of prostate tissue and colors, shapes, geometries,
locations of nuclei, stroma, vessels, glands and other cellular components were
generated by both models with structural similarity indices of 0.68 (staining)
and 0.84 (destaining). The proposed staining and destaining models can engender
computational H&E staining and destaining of WSRI biopsies without additional
equipment and devices.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning