DSTED: Decoupling Temporal Stabilization and Discriminative Enhancement for Surgical Workflow Recognition

• URL: http://arxiv.org/abs/2512.19387v1
• Date: Mon, 22 Dec 2025 13:36:26 GMT
• Title: DSTED: Decoupling Temporal Stabilization and Discriminative Enhancement for Surgical Workflow Recognition
• Authors: Yueyao Chen, Kai-Ni Wang, Dario Tayupo, Arnaud Huaulm'e, Krystel Nyangoh Timoh, Pierre Jannin, Qi Dou,
• Abstract summary: Surgical workflow recognition enables context-aware assistance and skill assessment in computer-assisted interventions.<n>Current methods suffer from two critical challenges: prediction jitter across consecutive frames and poor discrimination of ambiguous phases.<n>This paper aims to develop a stable framework by selectively propagating reliable historical information and explicitly modeling uncertainty for hard sample enhancement.
• Score: 13.734575975699963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Surgical workflow recognition enables context-aware assistance and skill assessment in computer-assisted interventions. Despite recent advances, current methods suffer from two critical challenges: prediction jitter across consecutive frames and poor discrimination of ambiguous phases. This paper aims to develop a stable framework by selectively propagating reliable historical information and explicitly modeling uncertainty for hard sample enhancement. Methods: We propose a dual-pathway framework DSTED with Reliable Memory Propagation (RMP) and Uncertainty-Aware Prototype Retrieval (UPR). RMP maintains temporal coherence by filtering and fusing high-confidence historical features through multi-criteria reliability assessment. UPR constructs learnable class-specific prototypes from high-uncertainty samples and performs adaptive prototype matching to refine ambiguous frame representations. Finally, a confidence-driven gate dynamically balances both pathways based on prediction certainty. Results: Our method achieves state-of-the-art performance on AutoLaparo-hysterectomy with 84.36% accuracy and 65.51% F1-score, surpassing the second-best method by 3.51% and 4.88% respectively. Ablations reveal complementary gains from RMP (2.19%) and UPR (1.93%), with synergistic effects when combined. Extensive analysis confirms substantial reduction in temporal jitter and marked improvement on challenging phase transitions. Conclusion: Our dual-pathway design introduces a novel paradigm for stable workflow recognition, demonstrating that decoupling the modeling of temporal consistency and phase ambiguity yields superior performance and clinical applicability.

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