We present a virtual staining framework that can rapidly stain defocused autofluorescence images of label-free tissue, matching the performance of standard virtual staining models that use in-focus unlabeled images. We trained and blindly tested this deep learning-based framework using human lung tissue. Using coarsely-focused autofluorescence images acquired with 4× fewer focus points and 2× lower focusing precision, we achieved equivalent performance to the standard virtual staining that used finely-focused autofluorescence input images. We achieved a ~32% decrease in the total image acquisition time needed for virtual staining of a label-free whole-slide image, alongside a ~89% decrease in the autofocusing time.
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