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001042604 0247_ $$2ISSN$$a2117-4458
001042604 0247_ $$2ISSN$$a2273-1709
001042604 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02590
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001042604 1001_ $$0P:(DE-Juel1)196697$$aKazimi, Bashir$$b0$$ufzj
001042604 1112_ $$aThe 17th European Microscopy Congress 2024$$cCopenhagen$$d2024-08-25 - 2024-08-30$$wDenmark
001042604 245__ $$aComparative Analysis of Self-Supervised Learning Techniques for Electron Microscopy Images
001042604 260__ $$aLes Ulis$$bEDP Sciences$$c2024
001042604 300__ $$a202412910037
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001042604 4900_ $$aBIO Web of Conferences
001042604 520__ $$aDeep learning has revolutionized a wide array of tasks across differentdomains, including electron microscopy (EM) image analysis, by leveraginglarge labeled datasets for training. However, the scarcity of such labeleddatasets in EM necessitates the exploration of alternative methods. Self-supervised learning (SSL) emerges as a promising approach to leverageunlabeled data, featuring techniques such as, e.g., masked image modeling(MIM) — which predicts missing parts of the input data, as well as contrastivelearning — which learns by distinguishing between similar and dissimilar pairsof data. This study aims to investigate the impact of these SSL techniques onEM images, providing a case study on the effectiveness of leveragingunlabeled data in a domain where labeled datasets are limited and expensiveto create.
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001042604 7001_ $$0P:(DE-Juel1)186075$$aSandfeld, Stefan$$b1$$eCorresponding author$$ufzj
001042604 773__ $$0PERI:(DE-600)2673408-4$$a10.1051/bioconf/202412910037$$gVol. 129, p. 10037 -$$p10037 -$$v129$$x2117-4458$$y2024
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