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@INPROCEEDINGS{Kazimi:1042604,
      author       = {Kazimi, Bashir and Sandfeld, Stefan},
      title        = {{C}omparative {A}nalysis of {S}elf-{S}upervised {L}earning
                      {T}echniques for {E}lectron {M}icroscopy {I}mages},
      volume       = {129},
      issn         = {2117-4458},
      address      = {Les Ulis},
      publisher    = {EDP Sciences},
      reportid     = {FZJ-2025-02590},
      series       = {BIO Web of Conferences},
      pages        = {10037 -},
      year         = {2024},
      abstract     = {Deep learning has revolutionized a wide array of tasks
                      across differentdomains, including electron microscopy (EM)
                      image analysis, by leveraginglarge labeled datasets for
                      training. However, the scarcity of such labeleddatasets in
                      EM necessitates the exploration of alternative methods.
                      Self-supervised learning (SSL) emerges as a promising
                      approach to leverageunlabeled data, featuring techniques
                      such as, e.g., masked image modeling(MIM) — which predicts
                      missing parts of the input data, as well as
                      contrastivelearning — which learns by distinguishing
                      between similar and dissimilar pairsof data. This study aims
                      to investigate the impact of these SSL techniques onEM
                      images, providing a case study on the effectiveness of
                      leveragingunlabeled data in a domain where labeled datasets
                      are limited and expensiveto create.},
      month         = {Aug},
      date          = {2024-08-25},
      organization  = {The 17th European Microscopy Congress
                       2024, Copenhagen (Denmark), 25 Aug 2024
                       - 30 Aug 2024},
      cin          = {IAS-9},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1051/bioconf/202412910037},
      url          = {https://juser.fz-juelich.de/record/1042604},
}