Creating in-silico data with generative AI promises a cost-effective alternative to staining, imaging, and annotating whole slide images in computational pathology. Diffusion models are the state-of-the-art solution for generating in-silico images, offering unparalleled fidelity and realism. Using appearance transfer diffusion models allows for zero-shot image generation, facilitating fast application and making model training unnecessary. However current appearance transfer diffusion models are designed for natural images, where the main task is to transfer the foreground object from an origin to a target domain, while the background is of insignificant importance. In computational pathology, specifically in oncology, it is however not straightforward to define which objects in an image should be classified as foreground and background, as all objects in an image may be of critical importance for the detailed understanding the tumor micro-environment. We contribute to the applicability of appearance transfer diffusion models to immunohistochemistry-stained images by modifying the appearance transfer guidance to alternate between class-specific AdaIN feature statistics matchings using existing segmentation masks. The performance of the proposed method is demonstrated on the downstream task of supervised epithelium segmentation, showing that the number of manual annotations required for model training can be reduced by 75%, outperforming the baseline approach. Additionally, we consulted with a certified pathologist to investigate future improvements. We anticipate this work to inspire the application of zero-shot diffusion models in computational pathology, providing an efficient method to generate in-silico images with unmatched fidelity and realism, which prove meaningful for downstream tasks, such as training existing deep learning models or finetuning foundation models.

通过修改外观转移扩散模型的引导方式，使用现有分割掩模的类别特定AdaIN特征统计匹配，我们增加外观转移扩散模型在免疫组织化学染色图像中的适用性，实验证明模型训练所需的手动注释数量可以减少75%，超过基准方法，从而在监督上皮分割的下游任务上表现出色。预计这项工作将激发在计算病理学中应用零样本扩散模型的方法，提供一种生成具有无与伦比的保真度和逼真度的模拟图像的高效方法，这对于下游任务（例如训练现有的深度学习模型或微调基础模型）具有重要意义。

基于遮罩引导的跨图像注意力的零样本体外组织病理图像生成与扩散模型