HistoEncoder：一个用于前列腺癌的数字病理学基础模型
HistoEncoder: a digital pathology foundation model for prostate cancer
AI总结：
本文提出前列腺癌数字病理学基础模型HistoEncoder，通过4800万张组织切片图像预训练，在特征表示和下游任务中优于自然图像预训练模型，并展示了自动标注和改善生存模型两个低资源微调用例。
摘要：
基础模型通过在海量数据上进行训练来区分复杂模式，并且能够以最少的计算资源适应广泛的下游任务。在此，我们通过在大约4800万张前列腺组织切片图像上进行预训练，开发了一个用于前列腺癌数字病理学的基础模型，名为HistoEncoder。我们证明，从具有相似组织学模式的切片图像中提取的HistoEncoder特征在特征空间中会紧密地映射在一起。即使不进行微调，或者仅使用少1000倍的训练数据，HistoEncoder也优于使用自然图像预训练的模型。我们描述了两个利用HistoEncoder能力的用例，即通过使用有限的数据和计算资源对模型进行微调。首先，我们展示了如何利用HistoEncoder以高精度自动标注大规模数据集。其次，我们将组织学特征与常用的临床列线图相结合，显著改善了前列腺癌特异性死亡生存模型。像HistoEncoder这样的基础模型可以使资源有限的组织无需大量数据集或大量计算资源，就能构建有效的临床软件工具。
英文摘要：
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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