Nunes João D, Montezuma Diana, Oliveira Domingos, Pereira Tania, Zlobec Inti, Pinto Isabel Macedo, Cardoso Jaime S
Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal.
Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.
Sensors (Basel). 2025 Apr 30;25(9):2856. doi: 10.3390/s25092856.
Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.
由于苏木精和伊红（H&E）染色的全切片图像（WSIs）存在高度变异性、隐藏分层和批次效应，在计算病理学（CPath）的深度学习（DL）中，超出训练分布进行泛化是主要挑战之一。但是，尽管深度学习依赖大量多样且带注释的数据，但通常会有来自一个或多个源分布的大量带注释样本，以及另一个代表我们想要进行泛化的目标分布的部分带注释或未标记的数据集，即所谓的域适应（DA）。在这项工作中，我们专注于从单个源分布到目标域进行泛化的任务。由于目前仍不清楚哪种域适应策略最适合CPath，我们评估了三种不同的DA策略，即FixMatch、CycleGAN和一种自监督特征提取器，并表明DA在CPath中仍然是一项挑战。
