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Abstract By generating realistic histologically-stained images from label-free image data, virtual staining (VS) methods have the potential to streamline clinical workflows, improve image consistency and provide new ways of visualizing and analysing tissues. This article describes a new VS approach based on the application of conditional generative adversarial networks to translate high-resolution phase images of unstained tissues, recovered using Fourier ptychographic microscopy (FPM), into brightfield H&E images. Compared to other label-free imaging methods, FPM offers unique advantages for VS as it allows simultaneous capture of sample amplitude and phase information, simplifying the pixelwise registration required for supervised training. FPM combines high spatial resolution with a large field of view and a large depth of field making it well suited to large format imaging of histological tissues. The method is readily implemented by modifying a conventional brightfield microscope using simple, low-cost optoelectronic hardware. Using colonic polyps as a test case, we compare FPM and VS-FPM images to brightfield whole slide images (WSIs) captured using a pathology slide scanner. Our results show that FPM images captured at 4x magnification have a spatial resolution equivalent to WSIs captured at 20x magnification. Virtual H&E images of unstained tissues generated using VS-FPM closely match brightfield images of the same tissue sections captured after chemical staining, enabling pathological assessment and diagnosis. Highlights Fourier Ptychography (FPM) can capture large-format complex images of tissue sections. 4x FPM images and 20x images of H&E-stained tissues have equivalent spatial resolution. Conditional GANs can generate accurate brightfield H&E images from FPM phase images. VS-FPM enabled pathologists to assess and diagnose unstained colonic polyps. The method can be applied to other sample types in histo- and cyto-pathology. Graphical abstract