Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts. Addressing this requires domain generalization (DG) algorithms. However, a systematic evaluation of DG algorithms in the CPath context is lacking. This study aims to benchmark the effectiveness of 30 DG algorithms on 3 CPath tasks of varying difficulty through 7,560 cross-validation runs. We evaluate these algorithms using a unified and robust platform, incorporating modality-specific techniques and recent advances like pretrained foundation models. Our extensive cross-validation experiments provide insights into the relative performance of various DG strategies. We observe that self-supervised learning and stain augmentation consistently outperform other methods, highlighting the potential of pretrained models and data augmentation. Furthermore, we introduce a new pan-cancer tumor detection dataset (HISTOPANTUM) as a benchmark for future research. This study offers valuable guidance to researchers in selecting appropriate DG approaches for CPath tasks.

本研究解决了计算病理学中深度学习模型在应用于未见数据时性能下降的问题，缺乏对领域泛化算法的系统评估。通过对30种领域泛化算法在3个不同难度的计算病理学任务上的7560次交叉验证进行基准测试，发现自监督学习和染色增强 consistently 超过其他方法，同时引入了新的全癌症肿瘤检测数据集（HISTOPANTUM）作为未来研究的基准。

计算病理学中的领域泛化算法基准测试