ProtoFlow: 基于学习动态场景图原型的可解释且鲁棒的手术流程建模
ProtoFlow: Interpretable and Robust Surgical Workflow Modeling with Learned Dynamic Scene Graph Prototypes
AI总结：
ProtoFlow通过学习动态场景图原型，实现了手术流程的可解释和鲁棒建模，提升了AI在手术培训和决策支持中的应用潜力。
摘要：
目的：详细的手术识别对于推进AI辅助手术至关重要，但进展受到高标注成本、数据稀缺和缺乏可解释模型的阻碍。尽管场景图提供了手术事件的结构化抽象，但其全部潜力仍未被开发。在本工作中，我们引入了ProtoFlow，一种新的框架，通过学习动态场景图原型来以可解释和鲁棒的方式建模复杂的手术流程。方法：ProtoFlow利用图神经网络（GNN）编码器-解码器架构，结合自监督预训练进行丰富的表示学习和基于原型的微调阶段。这一过程发现并优化核心原型，这些原型封装了反复出现的、具有临床意义的手术交互模式，为流程分析提供可解释的基础。结果：我们在细粒度的CAT-SG数据集上评估了我们的方法。ProtoFlow不仅在总体准确率上优于标准GNN基线，而且在有限数据、少样本场景中表现出卓越的鲁棒性，即使在仅训练一个手术视频时也能保持强劲表现。我们的定性分析进一步表明，学习到的原型能够识别不同的手术子技术，并提供清晰的、可解释的流程偏差和罕见并发症的见解。结论：通过结合稳健的表示学习与内在的可解释性，ProtoFlow代表了向开发更透明、可靠和数据高效AI系统的重要一步，加速其在手术培训、实时决策支持和流程优化中的临床应用潜力。
英文摘要：
Purpose: Detailed surgical recognition is critical for advancing AI-assisted surgery, yet progress is hampered by high annotation costs, data scarcity, and a lack of interpretable models. While scene graphs offer a structured abstraction of surgical events, their full potential remains untapped. In this work, we introduce ProtoFlow, a novel framework that learns dynamic scene graph prototypes to model complex surgical workflows in an interpretable and robust manner. Methods: ProtoFlow leverages a graph neural network (GNN) encoder-decoder architecture that combines self-supervised pretraining for rich representation learning with a prototype-based fine-tuning stage. This process discovers and refines core prototypes that encapsulate recurring, clinically meaningful patterns of surgical interaction, forming an explainable foundation for workflow analysis. Results: We evaluate our approach on the fine-grained CAT-SG dataset. ProtoFlow not only outperforms standard GNN baselines in overall accuracy but also demonstrates exceptional robustness in limited-data, few-shot scenarios, maintaining strong performance when trained on as few as one surgical video. Our qualitative analyses further show that the learned prototypes successfully identify distinct surgical sub-techniques and provide clear, interpretable insights into workflow deviations and rare complications. Conclusion: By uniting robust representation learning with inherent explainability, ProtoFlow represents a significant step toward developing more transparent, reliable, and data-efficient AI systems, accelerating their potential for clinical adoption in surgical training, real-time decision support, and workflow optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
