探索大模型零样本泛化能力，提升3D医学影像分割跨域性能
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study
- 通过智能提示技术增强可提示大模型的跨域适应性
- 在12个数据集上验证6种大模型实现零样本域泛化
- 为医学影像分割提供新思路，适合研究跨模态泛化者参考
医学图像分割中的域偏移问题由成像模态和采集协议差异引起，限制了模型泛化能力。尽管在多样化大规模数据上训练的基础模型（FMs）具有零样本泛化的潜力，其在三维医学数据上的应用仍待深入探索。本研究通过涵盖6种医学分割基础模型和12个公开数据集的全面实验，评估其域泛化能力。结果表明，通过智能提示技术，可提示基础模型能有效缓解域间差距。同时，多维度分析揭示了基础模型在零样本域泛化中的可行性，并指明未来研究方向。
原文摘要 · Abstract (English)
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.
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