Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study
This addresses domain generalization for medical imaging, but it is an incremental experimental study rather than a novel method.
The study tackled the problem of domain shift in 3D medical image segmentation by evaluating six foundational models across 12 datasets, finding that promptable models can bridge domain gaps with smart prompting techniques.
Domain shift, caused by variations in imaging modalities and acquisition protocols, limits model generalization in medical image segmentation. While foundation models (FMs) trained on diverse large-scale data hold promise for zero-shot generalization, their application to volumetric medical data remains underexplored. In this study, we examine their ability towards domain generalization (DG), by conducting a comprehensive experimental study encompassing 6 medical segmentation FMs and 12 public datasets spanning multiple modalities and anatomies. Our findings reveal the potential of promptable FMs in bridging the domain gap via smart prompting techniques. Additionally, by probing into multiple facets of zero-shot DG, we offer valuable insights into the viability of FMs for DG and identify promising avenues for future research.