  Synthetic images can be used for the development and evaluation of deep
learning algorithms in the context of limited availability of data. In the
field of computational pathology, where histology images are large in size and
visual context is crucial, synthesis of large high resolution images via
generative modeling is a challenging task. This is due to memory and
computational constraints hindering the generation of large images. To address
this challenge, we propose a novel SAFRON (Stitching Across the FRONtiers)
framework to construct realistic, large high resolution tissue image tiles from
ground truth annotations while preserving morphological features and with
minimal boundary artifacts. We show that the proposed method can generate
realistic image tiles of arbitrarily large size after training it on relatively
small image patches. We demonstrate that our model can generate high quality
images, both visually and in terms of the Frechet Inception Distance. Compared
to other existing approaches, our framework is efficient in terms of the memory
requirements for training and also in terms of the number of computations to
construct a large high-resolution image. We also show that training on
synthetic data generated by SAFRON can significantly boost the performance of a
state-of-the-art algorithm for gland segmentation in colorectal cancer
histology images. Sample high resolution images generated using SAFRON are
available at the URL: https://warwick.ac.uk/TIALab/SAFRON
