ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos
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).
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSurgical phase recognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SparSTAR: Sparse Attention for SpaceTime AutoRegressive Video Synthesis
InfinityStar extends visual autoregressive generation to video through a sequence of image and clip pyramids. Its changing scale and cross-clip context, however, leave late-scale attention costly and make sparse patterns…
Video GenerationFriends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition
Automatic surgical phase recognition is a core technology for modern operating rooms and online surgical video assessment platforms. Current state-of-the-art methods use both spatial and temporal information to tackle th…
Action SegmentationOffline surgical phase recognitionOnline surgical phase recognitionSegmentation+1MuST: Multi-Scale Transformers for Surgical Phase Recognition
Phase recognition in surgical videos is crucial for enhancing computer-aided surgical systems as it enables automated understanding of sequential procedural stages. Existing methods often rely on fixed temporal windows f…
Online surgical phase recognitionSurgical phase recognitionLoViT: Long Video Transformer for Surgical Phase Recognition
Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they trai…
Online surgical phase recognitionSurgical phase recognitionSKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition
This paper introduces SKiT, a fast Key information Transformer for phase recognition of videos. Unlike previous methods that rely on complex models to capture long-term temporal information, SKiT accurately recognize…
Online surgical phase recognitionSurgical phase recognition