AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
Channel: David Kügler
Date: 2020-09-16

The presentation to The 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020).

Kügler, D*; Uecker, M*; Kuijper, A; Mukhopadhyay, A (2020): AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation. MICCAI 2020, Springer, doi: tbd, arXiv: 2006.14858
* Equal contribution

You can find more information to our work at https://autosnap.david-kuegler.de/

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The paper is also available at arXiv https://arxiv.org/abs/2006.14858

Abstract
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 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.