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@ARTICLE{Assalauova:473043,
      author       = {Assalauova, Dameli and Ignatenko, Alexandr and Isensee,
                      Fabian and Bobkov, Sergej and Darya, Trofimova and
                      Vartaniants, Ivan},
      title        = {{C}lassification of diffraction patterns using a
                      convolutional neural network in single particle imaging
                      experiments performed at {X}-ray free-electron lasers},
      journal      = {Journal of applied crystallography},
      volume       = {55},
      number       = {3},
      issn         = {0021-8898},
      address      = {[S.l.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {PUBDB-2021-05426},
      pages        = {444 - 454},
      year         = {2022},
      abstract     = {Abstract Single particle imaging (SPI) at X-ray free
                      electron lasers (XFELs) is particularly well suited to
                      determine the 3D structure of particles in their native
                      environment. For a successful reconstruction, diffraction
                      patterns originating from a single hit must be isolated from
                      a large number of acquired patterns. We propose to formulate
                      this task as an image classification problem and solve it
                      using convolutional neural network (CNN) architectures. Two
                      CNN configurations are developed: one that maximises the
                      F1-score and one that emphasises high recall. We also
                      combine the CNNs with expectation maximization (EM)
                      selection as well as size filtering. We observed that our
                      CNN selections have lower contrast in power spectral density
                      functions relative to the EM selection, used in our previous
                      work. However, the reconstruction of our CNN-based
                      selections gives similar results. Introducing CNNs into SPI
                      experiments allows streamlining the reconstruction pipeline,
                      enables researchers to classify patterns on the fly, and, as
                      a consequence, enables them to tightly control the duration
                      of their experiments. We think that bringing non-standard
                      artificial intelligence (AI) based solutions in a
                      well-described SPI analysis workflow may be beneficial for
                      the future development of the SPI experiments.},
      cin          = {FS-PS / FS-PETRA / $XFEL_DO_TS$},
      ddc          = {540},
      cid          = {I:(DE-H253)FS-PS-20131107 / I:(DE-H253)FS-PETRA-20140814 /
                      $I:(DE-H253)XFEL_DO_TS-20210408$},
      pnm          = {633 - Life Sciences – Building Blocks of Life: Structure
                      and Function (POF4-633)},
      pid          = {G:(DE-HGF)POF4-633},
      experiment   = {EXP:(DE-H253)XFEL-Exp-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35719305},
      UT           = {WOS:000810763300001},
      doi          = {10.1107/S1600576722002667},
      url          = {https://bib-pubdb1.desy.de/record/473043},
}