Summary of Swag: Long-term Surgical Workflow Prediction with Generative-based Anticipation, by Maxence Boels et al.
SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation
by Maxence Boels, Yang Liu, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes SWAG (Surgical Workflow Anticipative Generation), a framework that combines phase recognition and anticipation for surgical workflow guidance. The approach uses a generative model to predict sequences of future surgical phases at minute intervals over long horizons. Two decoding methods are explored: single-pass (SP) and auto-regressive (AR). A novel embedding approach is also proposed, using prior knowledge to enhance the accuracy of phase anticipation. The framework’s performance is evaluated on the Cholec80 and AutoLaparo21 datasets, demonstrating improved accuracy compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgical procedures require anticipating future steps while performing surgery. Current approaches can only recognize current phases but not provide foresight or guidance for future procedural steps. This paper introduces SWAG, a new framework that combines phase recognition and anticipation to guide surgical workflows. It uses generative models to predict sequences of future surgical phases at minute intervals over long horizons. Two methods are tested: single-pass (SP) and auto-regressive (AR). The results show that SWAG outperforms existing methods on the Cholec80 and AutoLaparo21 datasets. |
Keywords
» Artificial intelligence » Embedding » Generative model
