  Virtual staining streamlines traditional staining procedures by digitally
generating stained images from unstained or differently stained images. While
conventional staining methods involve time-consuming chemical processes,
virtual staining offers an efficient and low infrastructure alternative.
Leveraging microscopy-based techniques, such as confocal microscopy,
researchers can expedite tissue analysis without the need for physical
sectioning. However, interpreting grayscale or pseudo-color microscopic images
remains a challenge for pathologists and surgeons accustomed to traditional
histologically stained images. To fill this gap, various studies explore
digitally simulating staining to mimic targeted histological stains. This paper
introduces a novel network, In-and-Out Net, specifically designed for virtual
staining tasks. Based on Generative Adversarial Networks (GAN), our model
efficiently transforms Reflectance Confocal Microscopy (RCM) images into
Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM
images using aluminum chloride preprocessing for skin tissues. Training the
model with virtual H\&E labels featuring two fluorescence channels eliminates
the need for image registration and provides pixel-level ground truth. Our
contributions include proposing an optimal training strategy, conducting a
comparative analysis demonstrating state-of-the-art performance, validating the
model through an ablation study, and collecting perfectly matched input and
ground truth images without registration. In-and-Out Net showcases promising
results, offering a valuable tool for virtual staining tasks and advancing the
field of histological image analysis.
