%0 Conference Paper
%A Kazimi, Bashir
%A Sandfeld, Stefan
%T Comparative Analysis of Self-Supervised Learning Techniques for Electron Microscopy Images
%V 129
%@ 2117-4458
%C Les Ulis
%I EDP Sciences
%M FZJ-2025-02590
%B BIO Web of Conferences
%P 10037 -
%D 2024
%X 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.
%B The 17th European Microscopy Congress 2024
%C 25 Aug 2024 - 30 Aug 2024, Copenhagen (Denmark)
Y2 25 Aug 2024 - 30 Aug 2024
M2 Copenhagen, Denmark
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%R 10.1051/bioconf/202412910037
%U https://juser.fz-juelich.de/record/1042604