Benchmarking Domain Generalization Algorithms in Computational Pathology

The study on benchmarking domain generalization algorithms in computational pathology addresses a critical gap in evaluating the performance of these algorithms when faced with unseen data. By systematically assessing 30 DG algorithms across three tasks through an extensive 7,560 cross-validation runs, the research reveals that self-supervised learning and stain augmentation are particularly effective, underscoring the potential of pretrained models and data augmentation techniques. This work not only enhances understanding of DG strategies but also introduces the HISTOPANTUM dataset, which serves as a new benchmark for future studies in computational pathology. The insights gained from this research are crucial for guiding researchers in selecting appropriate DG approaches, ultimately improving the robustness of deep learning models in clinical applications.