Using 4x fewer focus points with 2x lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images.
To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual-autofocusing network through a style loss during the training. This framework incorporates a virtual-autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue.
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