Label-free multiple histochemical virtual staining enabled by multiwavelength autofluorescence microscopy and deep learning

In this work, a multiwavelength autofluorescence virtual instant stain (MAVIS) workflow is proposed to provide a multiple virtual staining solution to facilitate clinical diagnosis. Multiple ultraviolet excitation and visible emission wavelengths are used to highlight different biomolecules while a weakly supervised algorithm provides a robust and accurate virtual staining with adjacent tissue slices. The result of MAVIS with three histochemical stains on human tissue slices achieves a multi-scale structural similarity index measure > 0.6, demonstrating the potential of multiple virtual staining as a rapid and low-cost alternative to the current histological workflow.