Minimally invasive surgery mainly consists of a series of sub-tasks, which can be decomposed into basic gestures or contexts. As a prerequisite of autonomic operation, surgical gesture recognition can assist motion planning and decision-making, and build up context-aware knowledge to improve the surgical robot control quality. In this work, we aim to develop an effective surgical gesture recognition approach with an explainable feature extraction process. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is proposed in this paper, while spatial feature extraction is implemented via fine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed based on the VGG architecture. To eliminate the black-box effects of DCNN, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results. The proposed method was evaluated based on the suturing task with data obtained from the public available JIGSAWS database. Comparative studies were conducted to verify the proposed framework. Results indicated that the testing accuracy for the suturing task based on our proposed method is 87.13%, which outperforms most of the state-of-the-art algorithms.

中文翻译：

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基于可解释空间特征提取的双向多层独立RNN的手术手势识别

微创手术主要由一系列子任务组成，这些子任务可以分解为基本手势或上下文。作为自主操作的先决条件，手术姿势识别可以协助运动计划和决策，并建立上下文相关知识，以提高手术机器人的控制质量。在这项工作中，我们旨在开发一种具有可解释特征提取过程的有效手术姿势识别方法。提出了一种双向多层独立RNN（BML-indRNN）模型，通过基于VGG架构构造的深度卷积神经网络（DCNN）模型的微调来实现空间特征提取。为了消除DCNN的黑盒效应，采用了梯度加权类激活映射（Grad-CAM）。通过显示与手术手势分类结果有密切关系的手术图像区域，可以提供可解释的结果。基于从公开的JIGSAWS数据库获得的缝合任务，对提出的方法进行了评估。进行了比较研究以验证提议的框架。结果表明，基于我们提出的方法，缝合任务的测试准确性为87.13％，优于大多数最新算法。