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@INPROCEEDINGS{Martel:280927,
      author       = {Kügler, David and Uecker, Marc and Kuijper, Arjan and
                      Mukhopadhyay, Anirban},
      editor       = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov,
                      Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S.
                      Kevin and Racoceanu, Daniel and Joskowicz, Leo},
      title        = {{A}uto{SNAP}: {A}utomatically {L}earning {N}eural
                      {A}rchitectures for {I}nstrument {P}ose {E}stimation},
      volume       = {12263},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {DZNE-2025-01010},
      isbn         = {978-3-030-59715-3 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {375 - 384},
      year         = {2020},
      comment      = {Medical 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},
      booktitle     = {Medical 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},
      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.},
      month         = {Oct},
      date          = {2020-10-04},
      organization  = {Medical Imaging Computing and Computer
                       Assisted Intervention, Lima (Peru), 4
                       Oct 2020 - 8 Oct 2020},
      cin          = {AG Reuter},
      cid          = {I:(DE-2719)1040310},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-030-59716-0_36},
      url          = {https://pub.dzne.de/record/280927},
}