no code implementations • 14 May 2024 • Zhen Chen, Xingjian Luo, Jinlin Wu, Danny T. M. Chan, Zhen Lei, Jinqiao Wang, Sebastien Ourselin, Hongbin Liu
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.
