用4800万张前列腺组织切片训练出的病理基础模型，可高效辅助癌症诊断。
HistoEncoder: a digital pathology foundation model for prostate cancer
- 基于4800万张组织切片预训练，学习复杂病理模式
- 无需微调或仅需千分之一数据量即超越自然图像预训练模型
- 适合资源有限的机构快速构建临床辅助工具
基础模型通过海量数据训练以识别复杂模式，可低成本适配多种下游任务。本文提出针对前列腺癌数字病理学的HistoEncoder模型，该模型在4800万张前列腺组织切片上进行预训练。我们发现，具有相似组织学特征的切片在特征空间中距离更近。HistoEncoder在未微调或仅使用千分之一训练数据时，表现优于在自然图像上预训练的模型。我们展示了两个应用场景：一是仅用少量数据和算力微调即可高精度自动标注大规模数据集；二是将组织学特征与常用临床列线图结合，显著提升前列腺癌特异性死亡生存预测模型性能。此类基础模型使资源有限的机构也能无需大量数据或算力即可开发有效临床软件。
原文摘要 · Abstract (English)
Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that HistoEncoder features extracted from tile images with similar histological patterns map closely together in the feature space. HistoEncoder outperforms models pre-trained with natural images, even without fine-tuning or with 1000 times less training data. We describe two use cases that leverage the capabilities of HistoEncoder by fine-tuning the model with a limited amount of data and computational resources. First, we show how HistoEncoder can be used to automatically annotate large-scale datasets with high accuracy. Second, we combine histomics with commonly used clinical nomograms, significantly improving prostate cancer-specific death survival models. Foundation models such as HistoEncoder can allow organizations with limited resources to build effective clinical software tools without needing extensive datasets or significant amounts of computing.
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