Title: Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data Show Chinese title

Title: 数字病理学的原型引导扩散：利用最少的临床数据实现基础模型性能

Abstract: Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images. However, there is limited transparency into what drives the correlation between dataset size and performance, raising the question of whether simply adding more data to increase performance is always necessary. In this study, we propose a prototype-guided diffusion model to generate high-fidelity synthetic pathology data at scale, enabling large-scale self-supervised learning and reducing reliance on real patient samples while preserving downstream performance. Using guidance from histological prototypes during sampling, our approach ensures biologically and diagnostically meaningful variations in the generated data. We demonstrate that self-supervised features trained on our synthetic dataset achieve competitive performance despite using ~60x-760x less data than models trained on large real-world datasets. Notably, models trained using our synthetic data showed statistically comparable or better performance across multiple evaluation metrics and tasks, even when compared to models trained on orders of magnitude larger datasets. Our hybrid approach, combining synthetic and real data, further enhanced performance, achieving top results in several evaluations. These findings underscore the potential of generative AI to create compelling training data for digital pathology, significantly reducing the reliance on extensive clinical datasets and highlighting the efficiency of our approach. Show Chinese abstract

Abstract: 数字病理学中的基础模型利用大规模数据集来学习复杂组织学图像的有用紧凑特征表示。 然而，对于驱动数据集规模与性能之间相关性的因素缺乏透明性，这引发了是否简单地通过增加更多数据来提升性能总是必要的疑问。 在这项研究中，我们提出了一种基于原型引导的扩散模型，以大规模生成高保真合成病理学数据，从而实现大规模自监督学习，并减少对真实患者样本的依赖，同时保持下游性能。 通过在采样过程中从组织学原型获取指导，我们的方法确保生成数据在生物学和诊断上具有有意义的变化。 我们证明了，尽管使用的数据量比在大型现实世界数据集上训练的模型少约60倍至760倍，但在我们的合成数据集上训练的自监督特征仍能实现竞争性性能。 值得注意的是，使用我们的合成数据训练的模型在多个评估指标和任务上的表现统计上可比甚至更好，即使与在数量级更大的数据集上训练的模型相比也是如此。 我们的混合方法结合了合成数据和真实数据，进一步提升了性能，在多项评估中取得了最佳结果。 这些发现强调了生成式人工智能在为数字病理学创建有说服力的训练数据方面的潜力，显著减少了对庞大临床数据集的依赖，并突显了我们方法的效率。