AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
This work addresses instrument pose estimation for surgical applications, representing an incremental improvement in domain-specific neural architecture search.
The paper tackles the problem of pose estimation of surgical instruments in Computer Assisted Intervention by proposing AutoSNAP, an automatic framework that discovers neural architectures, resulting in SNAPNet which outperforms hand-engineered i3PosNet and state-of-the-art DARTS.
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.