Stabilizing Temporal Inference in Online Surgical Phase Recognition
A new study from arXiv (2605.16387) identifies two mechanisms behind temporal instability in Online Surgical Phase Recognition (SPR) models: early misclassifications causing error cascades and reliance on memoryless frame-wise decisions during phase transitions. The authors propose a unified Train-Inference-Evaluation framework with plug-and-play components, including a Temporal Error-Cascade (TEC) loss for training and an Evidence module for inference, to stabilize predictions without sacrificing accuracy.
Key facts
- Online SPR models achieve high frame-wise accuracy but lack temporal stability.
- Instability arises from early misclassifications propagating forward and memoryless frame-wise decisions.
- A unified Train-Inference-Evaluation framework is proposed.
- Temporal Error-Cascade (TEC) loss suppresses error onset during training.
- Evidence module stabilizes inference dynamics.
- Components are model-agnostic and plug-and-play.
- Study published on arXiv with ID 2605.16387.
- Focus is on surgical workflow understanding and downstream assistance.
Entities
Institutions
- arXiv