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In this work, we propose a few-shot colorectal tissue image generation methodfor addressing the scarcity of histopathological training data for rare cancertissues. Our few-shot generation method, named XM-GAN, takes one base and apair of reference tissue images as input and generates high-quality yet diverseimages. Within our XM-GAN, a novel controllable fusion block densely aggregateslocal regions of reference images based on their similarity to those in thebase image, resulting in locally consistent features. To the best of ourknowledge, we are the first to investigate few-shot generation in colorectaltissue images. We evaluate our few-shot colorectral tissue image generation byperforming extensive qualitative, quantitative and subject specialist(pathologist) based evaluations. Specifically, in specialist-based evaluation,pathologists could differentiate between our XM-GAN generated tissue images andreal images only 55% time. Moreover, we utilize these generated images as dataaugmentation to address the few-shot tissue image classification task,achieving a gain of 4.4% in terms of mean accuracy over the vanilla few-shotclassifier. Code: \url{https://github.com/VIROBO-15/XM-GAN}

Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification