文献类型： 会议论文
第一作者： Kevin Feghoul
作者： Kevin Feghoul 1 ; Deise Santana Maia 2 ; Mehdi El Amrani 3 ; Mohamed Daoudi 4 ; Ali Amad 5 ;
作者机构： 1.Univ. Lille, Inserm, CHU Lille, UMR-S1172 LilNCog, Lille, France|Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, Lille, France
2.Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, Lille, France
3.Department of Digestive Surgery and Transplantation, CHU Lille, PRESAGE, Univ. Lille, France
4.Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, Lille, France|IMT Nord Europe, Institut Mines-Télécom, Centre for Digital Systems, Lille, France
5.Univ. Lille, Inserm, CHU Lille, UMR-S1172 LilNCog, Lille, France
关键词： Training;Visualization;Surgery;Gesture recognition;Kinematics;Machine learning;Predictive models
会议名称： IEEE International Conference on Automatic Face and Gesture Recognition
主办单位：
页码： 1-10
摘要： Automatic surgical gesture recognition has the potential to revolutionize the field of surgery by enhancing patient care, surgical training, and our understanding of surgical skills. By integrating kinematic data, which precisely captures hand movements, with video data for contextual understanding, multimodal machine learning can greatly enhance the accuracy of surgical gesture recognition systems by capturing complementary knowledge. Recent research has highlighted the capabilities of Transformer-based models for temporal action segmentation. A key component of these models is the iterative refinement module, which enhances predictions using contextual data. In this study, we propose MGRFormer, a novel multimodal framework that leverages the interaction between kinematics and visual data at the refinement stage for the task of surgical gesture recognition. We evaluated our MGRFormer on the VTS dataset, and the results demonstrated that our approach outperformed unimodal and multimodal state-of-the-art methods by a large margin.
分类号： tp7
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Spatial-Temporal Graph Transformer for Surgical Skill Assessment in Simulation Sessions
作者：Kevin Feghoul;Deise Santana Maia;Mehdi El Amrani;Mohamed Daoudi;Ali Amad
关键词：Graph convolutional networks;Transformer;Surgical skill assessment;Hand skeleton;Simulation;Education
