SR-Mamba: Effective Surgical Phase Recognition with State Space Model

• URL: http://arxiv.org/abs/2407.08333v1
• Date: Thu, 11 Jul 2024 09:34:31 GMT
• Title: SR-Mamba: Effective Surgical Phase Recognition with State Space Model
• Authors: Rui Cao, Jiangliu Wang, Yun-Hui Liu,
• Abstract summary: SR-Mamba is 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. SR-Mamba establishes a new benchmark in surgical video analysis by demonstrating state-of-the-art performance on the Cholec80 and CATARACTS Challenge datasets.
• Score: 42.766718651973726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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