Summary of Sedmamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-coarse Temporal Fusion For Efficient Error Detection in Robot-assisted Surgery, by Jialang Xu et al.
SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery
by Jialang Xu, Nazir Sirajudeen, Matthew Boal, Nader Francis, Danail Stoyanov, Evangelos Mazomenos
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel hierarchical model named SEDMamba for automated detection of surgical errors in robotic-assisted surgery. The method incorporates the selective state space model (SSM) and enhances it with a bottleneck mechanism and fine-to-coarse temporal fusion (FCTF) to efficiently model long sequences while maintaining computational efficiency. This approach allows for the detection and temporal localization of surgical errors in long videos, which is crucial for improving robotic-assisted surgery outcomes. The proposed method outperforms state-of-the-art methods by at least 1.82% AUC and 3.80% AP performance gains with significantly reduced computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors use robots to perform surgeries better. Right now, there are challenges in detecting errors that happen over a long period of time while still being efficient on the computer. The authors propose a new model called SEDMamba that uses a special type of model called selective state space model (SSM) and makes it more powerful by adding some extra steps. This allows them to detect and pinpoint where mistakes are happening in videos of surgeries, which can help improve outcomes. They also release data and code so other researchers can use their method. |
Keywords
» Artificial intelligence » Auc
