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Self-supervised pseudo-colorizing of masked cells

Wagner, Royden ORCID iD icon 1; Lopez, Carlos Fernandez ORCID iD icon 1; Stiller, Christoph 1
1 Institut für Mess- und Regelungstechnik (MRT), Karlsruher Institut für Technologie (KIT)


Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. ... mehr

Volltext §
DOI: 10.5445/IR/1000162175
Veröffentlicht am 14.09.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik (MRT)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2023
Sprache Englisch
Identifikator KITopen-ID: 1000162175
Umfang 14 S.
Vorab online veröffentlicht am 12.02.2023
Nachgewiesen in arXiv
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