ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos
This work addresses phase recognition for surgical workflow analysis in computer-assisted intervention, presenting an incremental improvement over existing transformer-based methods.
The paper tackles surgical phase recognition from laparoscopic videos by proposing an Auto-Regressive Surgical Transformer (ARST) that models inter-phase correlations implicitly, achieving state-of-the-art performance on the Cholec80 dataset with an inference rate of 66 fps.
Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modeling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps).