Objective:

To introduce ProtoFlow, a prototype learning framework for surgical workflow modeling that aims to enhance interpretability and generalizability using dynamic scene graphs.

Approach:

Key Findings:

• ProtoFlow outperforms existing baselines in surgical workflow recognition on the CAT-SG dataset.
• The framework generalizes across different surgical procedures.

Interpretation:

Limitations:

• The approach may still face challenges related to data scarcity and generalization across diverse surgical contexts.

Conclusion:

ProtoFlow provides a structured framework for surgical workflow modeling.

Source

Based on findings from:

ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes

Felix Holm, Ghazal Ghazaei, Nassir Navab. Int. Journal Of Computer Assisted Radiology And Surgery, 2026.

https://link.springer.com/article/10.1007/s11548-026-03697-x

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