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000280927 020__ $$a978-3-030-59715-3 (print)
000280927 020__ $$a978-3-030-59716-0 (electronic)
000280927 0247_ $$2doi$$a10.1007/978-3-030-59716-0_36
000280927 0247_ $$2ISSN$$a0302-9743
000280927 0247_ $$2ISSN$$a1611-3349
000280927 037__ $$aDZNE-2025-01010
000280927 1001_ $$00000-0003-1375-5501$$aMartel, Anne L.$$b0$$eEditor
000280927 1112_ $$aMedical Imaging Computing and Computer Assisted Intervention$$cLima$$d2020-10-04 - 2020-10-08$$gMICCAI 2020$$wPeru
000280927 245__ $$aAutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
000280927 260__ $$aCham$$bSpringer International Publishing$$c2020
000280927 29510 $$aMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 / Martel, Anne L. (Editor) [https://orcid.org/0000-0003-1375-5501] ; Cham : Springer International Publishing, 2020, Chapter 36 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-59715-3=978-3-030-59716-0 ; doi:10.1007/978-3-030-59716-0
000280927 300__ $$a375 - 384
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000280927 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1758102367_31851
000280927 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000280927 4900_ $$aLecture Notes in Computer Science$$v12263
000280927 520__ $$aDespite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments. Currently, neural architectures for classification and segmentation tasks are adopted ignoring significant discrepancies between CAI and these tasks. We propose an automatic framework (AutoSNAP) for instrument pose estimation problems, which discovers and learns architectures for neural networks. We introduce 1) an efficient testing environment for pose estimation, 2) a powerful architecture representation based on novel Symbolic Neural Architecture Patterns (SNAPs), and 3) an optimization of the architecture using an efficient search scheme. Using AutoSNAP, we discover an improved architecture (SNAPNet) which outperforms both the hand-engineered i3PosNet and the state-of-the-art architecture search method DARTS.
000280927 536__ $$0G:(DE-HGF)POF4-354$$a354 - Disease Prevention and Healthy Aging (POF4-354)$$cPOF4-354$$fPOF IV$$x0
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000280927 7001_ $$00000-0002-7259-8609$$aAbolmaesumi, Purang$$b1$$eEditor
000280927 7001_ $$00000-0002-0980-3227$$aStoyanov, Danail$$b2$$eEditor
000280927 7001_ $$00000-0002-2252-8717$$aMateus, Diana$$b3$$eEditor
000280927 7001_ $$00000-0002-1147-766X$$aZuluaga, Maria A.$$b4$$eEditor
000280927 7001_ $$00000-0002-6881-4444$$aZhou, S. Kevin$$b5$$eEditor
000280927 7001_ $$00000-0002-9416-1803$$aRacoceanu, Daniel$$b6$$eEditor
000280927 7001_ $$00000-0002-3010-4770$$aJoskowicz, Leo$$b7$$eEditor
000280927 7001_ $$0P:(DE-2719)2814290$$aKügler, David$$b8
000280927 7001_ $$aUecker, Marc$$b9
000280927 7001_ $$00000-0002-6413-0061$$aKuijper, Arjan$$b10
000280927 7001_ $$00000-0003-0669-4018$$aMukhopadhyay, Anirban$$b11
000280927 773__ $$a10.1007/978-3-030-59716-0_36
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000280927 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2814290$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b8$$kDZNE
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000280927 9201_ $$0I:(DE-2719)1040310$$kAG Reuter$$lArtificial Intelligence in Medicine$$x0
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