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024 7 _ |a 10.1051/bioconf/202412910037
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024 7 _ |a 2117-4458
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024 7 _ |a 2273-1709
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024 7 _ |a 10.34734/FZJ-2025-02590
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037 _ _ |a FZJ-2025-02590
082 _ _ |a 570
100 1 _ |a Kazimi, Bashir
|0 P:(DE-Juel1)196697
|b 0
|u fzj
111 2 _ |a The 17th European Microscopy Congress 2024
|c Copenhagen
|d 2024-08-25 - 2024-08-30
|w Denmark
245 _ _ |a Comparative Analysis of Self-Supervised Learning Techniques for Electron Microscopy Images
260 _ _ |a Les Ulis
|c 2024
|b EDP Sciences
300 _ _ |a 202412910037
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520 _ _ |a 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.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
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588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Sandfeld, Stefan
|0 P:(DE-Juel1)186075
|b 1
|e Corresponding author
|u fzj
773 _ _ |a 10.1051/bioconf/202412910037
|g Vol. 129, p. 10037 -
|0 PERI:(DE-600)2673408-4
|p 10037 -
|v 129
|y 2024
|x 2117-4458
856 4 _ |u https://juser.fz-juelich.de/record/1042604/files/bioconf_emc2024_10037.pdf
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