  Modern histopathology relies on the microscopic examination of thin tissue
sections stained with histochemical techniques, typically using brightfield or
fluorescence microscopy. However, the staining of samples can permanently alter
their chemistry and structure, meaning an individual tissue section must be
prepared for each desired staining contrast. This not only consumes valuable
tissue samples but also introduces delays in essential diagnostic timelines. In
this work, virtual histochemical staining is developed using label-free photon
absorption remote sensing (PARS) microscopy. We present a method that generates
virtually stained histology images that are indistinguishable from the gold
standard hematoxylin and eosin (H&E) staining. First, PARS label-free
ultraviolet absorption images are captured directly within unstained tissue
specimens. The radiative and non-radiative absorption images are then
preprocessed, and virtually stained through the presented pathway. The
preprocessing pipeline features a self-supervised Noise2Void denoising
convolutional neural network (CNN) as well as a novel algorithm for pixel-level
mechanical scanning error correction. These developments significantly enhance
the recovery of sub-micron tissue structures, such as nucleoli location and
chromatin distribution. Finally, we used a cycle-consistent generative
adversarial network CycleGAN architecture to virtually stain the preprocessed
PARS data. Virtual staining is applied to thin unstained sections of malignant
human skin and breast tissue samples. Clinically relevant details are revealed,
with comparable contrast and quality to gold standard H&E-stained images. This
work represents a crucial step to deploying label-free microscopy as an
alternative to standard histopathology techniques.
