基于动力学与不确定性感知的树状高斯过程分类器的视频手术技能评估
Video-based Surgical Skill Assessment Using Dynamics-and-Uncertainty-Aware Tree-based Gaussian Process Classifier
- K.N. Toosi University of Technology（霍塞尼·图西科技大学）
- Concordia University（康考迪亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种结合表示流CNN与动力学和不确定性感知树状高斯过程分类器的视频手术技能评估方法，在JIGSAWS和Cataract-LMM数据集上实现高准确率与低计算成本。
AI中文摘要：
所提出的流程将表示流卷积神经网络与动力学和不确定性感知的树状高斯过程分类器相结合。在该框架中，潜在运动动力学既被用作判别性表示，也被用作输入不确定性的来源，从而增强了对时间变化和异常运动转换的鲁棒性。与传统深度学习方法相比，所提出的策略需要更少的训练数据，并具有更高的计算效率。为了进一步提高分类性能，我们引入了新颖的语义感知复合核，该核能够有效捕获手术视频特征中嵌入的语义、流和动态信息。此外，开发了不确定性感知核以增强复合核框架的鲁棒性和实际适用性。所提出的方法在两个基准数据集上进行了评估，即JIGSAWS和Cataract-LMM（撕囊术）数据集。实验结果表明，该方法在两个数据集上均表现出强劲的性能，包括JIGSAWS上的LOSO和LOUO评估协议，包括JIGSAWS上的受试者独立LOUO协议，在该协议下框架达到了96.9%的平均准确率；同时报告了受试者内LOSO协议下的结果，以便与先前工作进行比较，在显著降低计算成本的同时实现了具有竞争力的准确率。总体而言，所提出的流程为基于视频的手术技能评估提供了一个高效且准确的框架。
英文摘要：
The proposed pipeline integrates a representation-flow convolutional neural network with a dynamics- and uncertainty-aware tree-based Gaussian Process classifier. In this framework, latent motion dynamics are exploited both as discriminative representations and as a source of input uncertainty, enhancing robustness against temporal variations and abnormal motion transitions. Compared with conventional deep learning approaches, the proposed strategy requires less training data and offers improved computational efficiency. To further improve classification performance, we introduce novel semantic-aware compound kernels that effectively capture semantic, flow, and dynamic information embedded in surgical video features. In addition, uncertainty-aware kernels are developed to strengthen the robustness and practical applicability of the compound kernel framework. The proposed method is evaluated on two benchmark datasets, namely the JIGSAWS and the Cataract-LMM (Capsulorhexis) datasets. Experimental results demonstrate strong performance across both datasets, including the LOSO and LOUO evaluation protocols on JIGSAWS, including the subject-independent LOUO protocol on JIGSAWS, on which the framework attains a mean accuracy of \ph{96.9}\%; results under the within-subject LOSO protocol are reported for comparability with prior work, achieving competitive accuracy while substantially reducing computational cost. Overall, the proposed pipeline provides an efficient and accurate framework for video-based surgical skill assessment.
