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Despite recent successes, the advances in Deep Learning have not yet beenfully translated to Computer Assisted Intervention (CAI) problems such as poseestimation of surgical instruments. Currently, neural architectures forclassification and segmentation tasks are adopted ignoring significantdiscrepancies between CAI and these tasks. We propose an automatic framework(AutoSNAP) for instrument pose estimation problems, which discovers and learnsthe architectures for neural networks. We introduce 1)~an efficient testingenvironment for pose estimation, 2)~a powerful architecture representationbased on novel Symbolic Neural Architecture Patterns (SNAPs), and 3)~anoptimization of the architecture using an efficient search scheme. UsingAutoSNAP, we discover an improved architecture (SNAPNet) which outperforms boththe hand-engineered i3PosNet and the state-of-the-art architecture searchmethod DARTS.

AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation