  Accurate and fast histological staining is crucial in histopathology,
impacting diagnostic precision and reliability. Traditional staining methods
are time-consuming and subjective, causing delays in diagnosis. Digital
pathology plays a vital role in advancing and optimizing histology processes to
improve efficiency and reduce turnaround times. This study introduces a novel
deep learning-based framework for virtual histological staining using photon
absorption remote sensing (PARS) images. By extracting features from PARS
time-resolved signals using a variant of the K-means method, valuable
multi-modal information is captured. The proposed multi-channel cycleGAN
(MC-GAN) model expands on the traditional cycleGAN framework, allowing the
inclusion of additional features. Experimental results reveal that specific
combinations of features outperform the conventional channels by improving the
labeling of tissue structures prior to model training. Applied to human skin
and mouse brain tissue, the results underscore the significance of choosing the
optimal combination of features, as it reveals a substantial visual and
quantitative concurrence between the virtually stained and the gold standard
chemically stained hematoxylin and eosin (H&E) images, surpassing the
performance of other feature combinations. Accurate virtual staining is
valuable for reliable diagnostic information, aiding pathologists in disease
classification, grading, and treatment planning. This study aims to advance
label-free histological imaging and opens doors for intraoperative microscopy
applications.
