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

翻译：合成图像可用于在数据有限的情况下开发和评估深层次学习算法。在计算病理学领域,组织图象在大小和视觉背景上都很大,因此,通过基因模型模型合成大型高分辨率图像是一项艰巨的任务。这是由于记忆和计算方面的制约因素,妨碍了生成大型图像。为了应对这一挑战,我们提议了一个新的SAFRON(跨Frontiers)框架,以便从地面真相说明中构建现实的大型高分辨率组织图象砖,同时保留形态特征和最低限度的边界文物。我们表明,拟议方法可以在相对小的图像补丁上培训后产生实际的、任意大尺寸的图像砖块。我们证明,我们的模型能够产生高质量的图像,包括视觉图像和Frechet Inception距离。与其他现有方法相比,我们的框架在培训的记忆要求方面以及构建大型高分辨率图像的计算数量方面是有效的。我们还表明,由SAFRCR生成的合成数据培训可以大大地提升其高比例图像分析的性能。我们表明,在SFRARI/RARC生成的高分辨率分析中,其高分辨率分析系统生成的ASAL-ROAL-ARCRA。