We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our approach first generates synthetic segmentation masks, subsequently used as conditions for the high-fidelity generative diffusion process. The proposed sampling method can be scaled up to any desired image size while only requiring small patches for fast training. Moreover, it can be parallelized more efficiently than previous large-content generation methods while avoiding tiling artefacts. The training leverages classifier-free guidance to augment a small, sparsely annotated dataset with unlabelled data. Our method alleviates unique challenges in histopathological imaging practice: large-scale information, costly manual annotation, and protective data handling. The biological plausibility of DiffInfinite data is validated in a survey by ten experienced pathologists as well as a downstream segmentation task. Furthermore, the model scores strongly on anti-copying metrics which is beneficial for the protection of patient data.

本文介绍了 DiffInfinite，一种分层扩散模型，可以生成任意大小的组织学图像，同时保留远程相关结构信息。论文提出了一种高保真生成扩散过程的方法，并弱化了组织病理学成像实践中独特的挑战：大规模信息、昂贵的手动注释和保护数据处理。

DiffInfinite：组织病理学中使用并行随机裂纹扩散的大规模掩膜图像合成