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AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
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.Comment: Accepted at MICCAI 2020 Preparing code for release at
https://github.com/MECLabTUDA/AutoSNA
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