SR-Mamba: Effective Surgical Phase Recognition with State Space Model
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910523025522688 |
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| author | Cao, Rui Wang, Jiangliu Liu, Yun-Hui |
| author_facet | Cao, Rui Wang, Jiangliu Liu, Yun-Hui |
| contents | Surgical phase recognition is crucial for enhancing the efficiency and safety of computer-assisted interventions. One of the fundamental challenges involves modeling the long-distance temporal relationships present in surgical videos. Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition. In SR-Mamba, we leverage a bidirectional Mamba decoder to effectively model the temporal context in overlong sequences. Moreover, the efficient optimization of the proposed Mamba decoder facilitates single-step neural network training, eliminating the need for separate training steps as in previous works. This single-step training approach not only simplifies the training process but also ensures higher accuracy, even with a lighter spatial feature extractor. Our SR-Mamba establishes a new benchmark in surgical video analysis by demonstrating state-of-the-art performance on the Cholec80 and CATARACTS Challenge datasets. The code is accessible at https://github.com/rcao-hk/SR-Mamba. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08333 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SR-Mamba: Effective Surgical Phase Recognition with State Space Model Cao, Rui Wang, Jiangliu Liu, Yun-Hui Computer Vision and Pattern Recognition Surgical phase recognition is crucial for enhancing the efficiency and safety of computer-assisted interventions. One of the fundamental challenges involves modeling the long-distance temporal relationships present in surgical videos. Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition. In SR-Mamba, we leverage a bidirectional Mamba decoder to effectively model the temporal context in overlong sequences. Moreover, the efficient optimization of the proposed Mamba decoder facilitates single-step neural network training, eliminating the need for separate training steps as in previous works. This single-step training approach not only simplifies the training process but also ensures higher accuracy, even with a lighter spatial feature extractor. Our SR-Mamba establishes a new benchmark in surgical video analysis by demonstrating state-of-the-art performance on the Cholec80 and CATARACTS Challenge datasets. The code is accessible at https://github.com/rcao-hk/SR-Mamba. |
| title | SR-Mamba: Effective Surgical Phase Recognition with State Space Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.08333 |