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

翻译：本文提出了一种面向结直肠组织的小样本图像生成方法，旨在解决罕见癌症组织病理学训练数据稀缺的问题。我们命名为XM-GAN的小样本生成方法以一张基图像和一对参考组织图像为输入，生成高质量且多样的图像。在XM-GAN中，一种新颖的可控融合模块基于参考图像区域与基图像区域的相似性，密集聚合参考图像的局部区域，从而产生局部一致的特征。据我们所知，我们是首个研究结直肠组织图像小样本生成的工作。我们通过广泛的定性、定量以及领域专家（病理学家）评估来验证所提出的小样本结直肠组织图像生成方法。具体而言，在基于专家的评估中，病理学家仅能在55%的情况下区分XM-GAN生成的组织图像与真实图像。此外，我们将生成的图像用作数据增强手段来解决小样本组织图像分类任务，相比原始小样本分类器，平均准确率提升了4.4%。代码：\url{https://github.com/VIROBO-15/XM-GAN}