医疗系统规模的计算病理学——基于三十亿图像的自监督基础模型
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
AI总结：
本研究利用超过30亿张病理图像预训练视觉基础模型，比较MAE与DINO算法，在六个临床任务上验证了病理预训练优于自然图像预训练，DINO泛化更佳，推动了计算病理学规模化发展。
摘要：
自监督学习的最新突破使得利用大规模未标注数据集训练视觉基础模型成为可能，这些模型能够泛化到各种下游任务。尽管这种训练范式非常适合标注稀缺的医学领域，但在医学领域，尤其是病理学中，大规模预训练尚未得到广泛研究。以往病理学中的自监督学习工作利用较小的数据集进行预训练和评估下游性能。本项目旨在训练最大的学术基础模型，并通过在大型临床病理数据集上预训练和评估下游性能，来基准测试最著名的自监督学习算法。我们收集了迄今为止最大的病理学数据集，包含来自超过42.3万张显微镜切片的超过30亿张图像。我们比较了使用掩码自编码器（MAE）和DINO算法对视觉Transformer模型进行预训练的效果。我们在来自三个解剖部位和两个机构的六个临床相关任务上评估了性能：乳腺癌检测、炎症性肠病检测、乳腺癌雌激素受体预测、肺腺癌EGFR突变预测以及肺癌免疫治疗反应预测。我们的结果表明，与在自然图像上预训练相比，在病理学数据上预训练对下游性能有益。此外，DINO算法在所有测试任务中均取得了更好的泛化性能。所呈现的结果标志着计算病理学研究的一个阶段变化，为基于十亿图像规模的大规模并行预训练的更高效能模型的新时代铺平了道路。
英文摘要：
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology, has not been extensively studied. Previous work in self-supervised learning in pathology has leveraged smaller datasets for both pre-training and evaluating downstream performance. The aim of this project is to train the largest academic foundation model and benchmark the most prominent self-supervised learning algorithms by pre-training and evaluating downstream performance on large clinical pathology datasets. We collected the largest pathology dataset to date, consisting of over 3 billion images from over 423 thousand microscopy slides. We compared pre-training of visual transformer models using the masked autoencoder (MAE) and DINO algorithms. We evaluated performance on six clinically relevant tasks from three anatomic sites and two institutions: breast cancer detection, inflammatory bowel disease detection, breast cancer estrogen receptor prediction, lung adenocarcinoma EGFR mutation prediction, and lung cancer immunotherapy response prediction. Our results demonstrate that pre-training on pathology data is beneficial for downstream performance compared to pre-training on natural images. Additionally, the DINO algorithm achieved better generalization performance across all tasks tested. The presented results signify a phase change in computational pathology research, paving the way into a new era of more performant models based on large-scale, parallel pre-training at the billion-image scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
