论文标题
AutoSNAP：自动学习仪器姿势估计的神经体系结构
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
论文作者
论文摘要
尽管最近取得了成功，但深度学习的进步尚未被完全转化为计算机辅助干预（CAI）问题，例如手术仪器的姿势估计。当前，采用用于分类和细分任务的神经体系结构无视CAI和这些任务之间的重大差异。我们为仪器姿势估计问题提出了一个自动框架（AutoSNAP），该问题发现并了解神经网络的体系结构。我们介绍了1）〜一个有效的姿势估计测试环境，2）基于新型符号神经体系结构模式（SNAPS）和3）强大的体系结构表示，使用有效的搜索方案对体系结构进行了优化。使用AutoSNAP，我们发现了一个改进的体系结构（Snapnet），该体系结构的表现均优于手工设计的i3posnet和最先进的架构搜索方法飞镖。
Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments. Currently, neural architectures for classification and segmentation tasks are adopted ignoring significant discrepancies between CAI and these tasks. We propose an automatic framework (AutoSNAP) for instrument pose estimation problems, which discovers and learns the architectures for neural networks. We introduce 1)~an efficient testing environment for pose estimation, 2)~a powerful architecture representation based on novel Symbolic Neural Architecture Patterns (SNAPs), and 3)~an optimization of the architecture using an efficient search scheme. Using AutoSNAP, we discover an improved architecture (SNAPNet) which outperforms both the hand-engineered i3PosNet and the state-of-the-art architecture search method DARTS.
