The need for automatic surgical skills assessment is increasing, especially
because manual feedback from senior surgeons observing junior surgeons is prone
to subjectivity and time consuming. Thus, automating surgical skills evaluation
is a very important step towards improving surgical practice. In this paper, we
designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by
extracting patterns in the surgeon motions performed in robotic surgery. The
proposed method is validated on the JIGSAWS dataset and achieved very
competitive results with 100% accuracy on the suturing and needle passing
tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate
its black-box effect using class activation map. This feature allows our method
to automatically highlight which parts of the surgical task influenced the
skill prediction and can be used to explain the classification and to provide
personalized feedback to the trainee.

本文通过设计一种卷积神经网络 (CNN) 来自动评估机器人手术中的外科医生技能，经过我们提出的方法在 JIGSAWS 数据集上得到了非常有竞争力的结果，并通过类激活图来解释各分类并提供个性化反馈