Quantitative oblique back-illumination microscopy (qOBM) is a novel technology for label-free imaging of thick (unsectioned) tissue specimens, demonstrating high spatial resolution and 3-D capabilities. The grayscale contrast however, is unfamiliar to pathologist and histotechnicians without familiarization, limiting its adoption. We used deep learning techniques to convert qOBM into virtual H&E, observing successful conversion of both healthy and tumor thick (unsectioned) specimens. Transfer learning was demonstrated on a second collection of qOBM and H&E images of human astrocytoma specimens. With some improvement in robustness and generalizability, we anticipate that this approach can find clinical application.
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