Kügler, David ; Uecker, Marc ; Kuijper, Arjan ; Mukhopadhyay, Anirban (2020)
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation.
23rd International Conference on Medical Image Computing an Computer-Assisted Intervention (MICCAI 2020). virtual Conference (04.-08.10.)
doi: 10.1007/978-3-030-59716-0_36
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Kügler, David ; Uecker, Marc ; Kuijper, Arjan ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | Springer |
| Buchtitel: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 |
| Veranstaltungstitel: | 23rd International Conference on Medical Image Computing an Computer-Assisted Intervention (MICCAI 2020) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 04.-08.10. |
| DOI: | 10.1007/978-3-030-59716-0_36 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Interventional techniques, Medical applications, Medical imaging, Deep learning |
| Zusätzliche Informationen: | Part of the Lecture Notes in Computer Science book series (LNCS, volume 12263) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 19 Okt 2020 08:27 |
| Letzte Änderung: | 04 Dez 2020 08:34 |
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