In the realm of automated robotic surgery and computer-assisted
interventions, understanding robotic surgical activities stands paramount.
Existing algorithms dedicated to surgical activity recognition predominantly
cater to pre-defined closed-set paradigms, ignoring the challenges of
real-world open-set scenarios. Such algorithms often falter in the presence of
test samples originating from classes unseen during training phases. To tackle
this problem, we introduce an innovative Open-Set Surgical Activity Recognition
(OSSAR) framework. Our solution leverages the hyperspherical reciprocal point
strategy to enhance the distinction between known and unknown classes in the
feature space. Additionally, we address the issue of over-confidence in the
closed set by refining model calibration, avoiding misclassification of unknown
classes as known ones. To support our assertions, we establish an open-set
surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we
also collect a novel dataset on endoscopic submucosal dissection for surgical
activity tasks. Extensive comparisons and ablation experiments on these
datasets demonstrate the significant outperformance of our method over existing
state-of-the-art approaches. Our proposed solution can effectively address the
challenges of real-world surgical scenarios. Our code is publicly accessible at
this https URL

在自动机器人手术和计算机辅助干预领域中，理解机器人手术活动至关重要。为了解决真实世界中的开放场景挑战，我们引入了一种创新的开集手术活动识别（OSSAR）框架，通过使用超球面互补点策略增强特征空间中已知和未知类之间的差异，并通过改进模型校准来避免未知类被误分类为已知类。通过公开的 JIGSAWS 数据集以及我们自己收集的内窥镜黏膜下剥离的手术活动数据集，我们对我们的方法进行了广泛的对比和割离实验，结果表明我们的方法在现有最先进方法上取得了显著的提升。我们的方案可以有效处理真实世界手术场景的挑战。我们的代码可以在此 https URL 上公开访问。