VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons
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arXiv
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| Format: | Preprint |
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2024
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| author | Chen, Zhen Luo, Xingjian Wu, Jinlin Chan, Danny T. M. Lei, Zhen Wang, Jinqiao Ourselin, Sebastien Liu, Hongbin |
| author_facet | Chen, Zhen Luo, Xingjian Wu, Jinlin Chan, Danny T. M. Lei, Zhen Wang, Jinqiao Ourselin, Sebastien Liu, Hongbin |
| contents | The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intelligent and versatile surgical assistant is expected to accurately understand the surgeon's intentions and accordingly conduct the specific tasks to support the surgical process. In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e.g., surgical scene analysis, surgical instrument detection, and segmentation on demand. Specifically, to achieve superior surgical multimodal understanding, we devise a mixture of projectors (MOP) module to align the surgical MLLM in VS-Assistant to balance the natural and surgical knowledge. Moreover, we devise a surgical Function-Calling Tuning strategy to enable the VS-Assistant to understand surgical intentions, and thus make a series of surgical function calls on demand to meet the needs of the surgeons. Extensive experiments on neurosurgery data confirm that our VS-Assistant can understand the surgeon's intention more accurately than the existing MLLM, resulting in overwhelming performance in textual analysis and visual tasks. Source code and models will be made public. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons Chen, Zhen Luo, Xingjian Wu, Jinlin Chan, Danny T. M. Lei, Zhen Wang, Jinqiao Ourselin, Sebastien Liu, Hongbin Computer Vision and Pattern Recognition The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intelligent and versatile surgical assistant is expected to accurately understand the surgeon's intentions and accordingly conduct the specific tasks to support the surgical process. In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e.g., surgical scene analysis, surgical instrument detection, and segmentation on demand. Specifically, to achieve superior surgical multimodal understanding, we devise a mixture of projectors (MOP) module to align the surgical MLLM in VS-Assistant to balance the natural and surgical knowledge. Moreover, we devise a surgical Function-Calling Tuning strategy to enable the VS-Assistant to understand surgical intentions, and thus make a series of surgical function calls on demand to meet the needs of the surgeons. Extensive experiments on neurosurgery data confirm that our VS-Assistant can understand the surgeon's intention more accurately than the existing MLLM, resulting in overwhelming performance in textual analysis and visual tasks. Source code and models will be made public. |
| title | VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.08272 |