GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos

• URL: http://arxiv.org/abs/2307.11081v1
• Date: Thu, 20 Jul 2023 17:57:04 GMT
• Title: GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
• Authors: Nisarg A. Shah, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel
• Abstract summary: We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
• Score: 57.93194315839009
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. However, the benefits of joint modeling of spatio-temporal features and long-range information are not taken in account. In this paper, we propose a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches. Our method incorporates a gated-temporal attention mechanism that intelligently combines short-term and long-term spatio-temporal feature representations. We extensively evaluate our approach on two cataract surgery video datasets, namely Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods. These results validate the suitability of our proposed approach for automated surgical step recognition. Our code is released at: https://github.com/nisargshah1999/GLSFormer

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