Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging

Deep learning models for medical image analysis often fail in clinical deployment due to domain shift from varied acquisition hardware and protocols. We present a comprehensive evaluation of various domain generalization (DG) techniques to mitigate this performance degradation. We evaluate six DG algorithms against a baseline on two distinct tasks using large, multi-institutional datasets: grading prostate cancer aggressiveness from MRI using the ProstateNet dataset and assessing breast density from mammograms using the DMIST dataset, using a leave-one-domain-out protocol. Our results show that DG methods, particularly those that explicitly regularize the learning process, improve out-of-domain generalization, but do not fully close the gap with in-domain performance. On the ProstateNet dataset, the FISH algorithm achieved the highest average out-of-domain AUROC (0.678), a statistically significant improvement over the baseline (0.613). We observed similar trends on the DMIST dataset. These findings underscore the necessity of incorporating DG strategies to develop clinically deployable AI models.