Cross-Dataset Generalization

Cross-dataset generalization studies whether a model trained on one dataset can work on data from different hospitals, populations, devices, languages, or environments. Researchers test this with external validation and leave-one-dataset-out benchmarks, and try to improve transfer through diverse multi-source training, domain adaptation, robust representations, data augmentation, and limited target-domain fine-tuning. Results often show a gap between strong in-dataset scores and performance on new sources, so evaluation increasingly considers calibration, class-specific errors, and decision thresholds as well as accuracy. A central tension is that methods that remove source-specific signals may also discard useful information, while differences in measurement, labels, and data quality can make apparent model failures—or gains—hard to interpret.