PixCell：数字病理图像的生成基础模型
PixCell: A generative foundation model for digital histopathology images
AI总结：
PixCell是首个针对数字病理图像的生成基础模型，通过扩散模型在大规模数据集上训练，实现隐私保护的数据生成和虚拟染色任务，提升病理学研究效率。
摘要：
病理切片的数字化已彻底改变了病理学，为癌症诊断和研究提供了大量数据集。自监督和视觉-语言模型已被证明能有效挖掘大规模病理数据集，以学习判别性表示。另一方面，病理学中存在独特的问题，如标注数据稀缺、数据共享的隐私法规以及本质上生成性的任务，如虚拟染色。生成模型能够合成逼真且多样的图像，通过图像合成提供了解决这些问题的有力方案。我们介绍了PixCell，这是首个针对病理图像的生成基础模型。PixCell是一个在PanCan-30M上训练的扩散模型，PanCan-30M是一个大规模、多样化的数据集，源自69,184张不同癌症类型的H&E染色整片图像。我们采用渐进式训练策略和基于自监督的条件设置，使我们能够在没有任何人工标注数据的情况下扩展训练。通过在真实切片上进行条件设置，合成图像能够捕捉真实数据的特性，并可用于小规模数据集的数据增强以提升分类性能。我们通过将PixCell应用于两个生成性下游任务：隐私保护的合成数据生成和虚拟IHC染色，证明了PixCell的基础通用性。PixCell的高保真度条件生成使机构能够使用其私有数据合成高度逼真、特定部位的替代图像，这些图像可以代替原始患者数据进行共享。此外，使用大致配对的H&E-IHC瓷砖数据集，我们学习了将PixCell的条件从H&E转换为多种IHC染色，允许从H&E输入生成IHC图像。我们的训练模型已公开发布，以加速计算病理学的研究。
英文摘要：
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have been shown to effectively mine large pathology datasets to learn discriminative representations. On the other hand, there are unique problems in pathology, such as annotated data scarcity, privacy regulations in data sharing, and inherently generative tasks like virtual staining. Generative models, capable of synthesizing realistic and diverse images, present a compelling solution to address these problems through image synthesis. We introduce PixCell, the first generative foundation model for histopathology images. PixCell is a diffusion model trained on PanCan-30M, a large, diverse dataset derived from 69,184 H&E-stained whole slide images of various cancer types. We employ a progressive training strategy and a self-supervision-based conditioning that allows us to scale up training without any human-annotated data. By conditioning on real slides, the synthetic images capture the properties of the real data and can be used as data augmentation for small-scale datasets to boost classification performance. We prove the foundational versatility of PixCell by applying it to two generative downstream tasks: privacy-preserving synthetic data generation and virtual IHC staining. PixCell's high-fidelity conditional generation enables institutions to use their private data to synthesize highly realistic, site-specific surrogate images that can be shared in place of raw patient data. Furthermore, using datasets of roughly paired H&E-IHC tiles, we learn to translate PixCell's conditioning from H&E to multiple IHC stains, allowing the generation of IHC images from H&E inputs. Our trained models are publicly released to accelerate research in computational pathology.
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