The goal of oncologic surgeries is to completely resect tumor tissue, yet in up to 40% of such surgeries, positive marginsare found in the resected tissues. Postoperative histology using H&E-stained brightfield microscopy is the gold standard for determining margin status, but rapid frozen section analysis is sometimes performed for intraoperative guidance, albeit with inaccuracies. In this work, we introduce a virtual histological imaging method based on a non-contact, reflection-mode ultraviolet photoacoustic remote sensing and scattering microscope, combined with deep learning using a cycle-consistent generative adversarial network. The system is capable of high-resolution scanning with 390 nm resolution comparable toconventional histopathology, and fast widefield scanning, generating images with histological realism in freshly-resected thick tissues or thin sections. Cytologic and architectural features of interest are readily identifiable in virtual histology images of benign and malignant tissues. To evaluate system performance, a blinded study of pathologists rating image quality metrics was conducted, with our virtual histology approach offering preferred hematoxylin-like detail (P=0.0018) and overall stain quality (P=0.0321) compared to frozen section analysis.