Contribution to a conference proceedings/Contribution to a book

FZJ-2025-02590

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Comparative Analysis of Self-Supervised Learning Techniques for Electron Microscopy Images

Kazimi, B.FZJ* ; Sandfeld, S. (Corresponding author)FZJ*

2024
EDP Sciences Les Ulis

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Please use a persistent id in citations: doi:10.1051/bioconf/202412910037  doi:10.34734/FZJ-2025-02590

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.

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Contributing Institute(s):

1. Materials Data Science and Informatics (IAS-9)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

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