论文标题
为什么深层手术模型失败？：通过稳健性重新审视手术动作三胞胎识别
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
论文作者
论文摘要
手术动作三胞胎识别提供了对手术场景的更好理解。此任务具有很高的相关性，因为它为外科医生提供了上下文感知的支持和安全性。当前改善绩效的首选策略是开发新的网络机制。但是，当前最新技术的性能大大低于其他手术任务。为什么会发生这种情况？这是我们在这项工作中解决的问题。我们提出了第一项研究，以了解现有深度学习模型的失败，并通过鲁棒性和解释性的镜头来了解。首先，我们通过对抗性优化方案研究了当前的现有模型。然后，我们通过基于功能的解释分析故障模式。我们的研究表明，提高性能和提高可靠性的关键是核心和虚假属性。我们的工作为外科数据科学中更可信赖和可靠的深度学习模型打开了大门。
Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of current state-of-the-art techniques is substantially lower than other surgical tasks. Why is this happening? This is the question that we address in this work. We present the first study to understand the failure of existing deep learning models through the lens of robustness and explainability. Firstly, we study current existing models under weak and strong $δ-$perturbations via an adversarial optimisation scheme. We then analyse the failure modes via feature based explanations. Our study reveals that the key to improving performance and increasing reliability is in the core and spurious attributes. Our work opens the door to more trustworthy and reliable deep learning models in surgical data science.
