Author

Daniel Spengler

Delft University of TechnologyORCID 0009-0004-4118-7761Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:imaging· 2 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Approach:deep learning· 1

Frequent Co-Authors

Carlas SmithiD· 2 shared Serafim KoroviniD· 2 shared Anna Pals· 1 shared Dylan KalisvaartiD· 1 shared Gerben Rijpkema· 1 shared Hayri Emrah BalciogluiD· 1 shared Jaap van der WeerdiD· 1 shared Kirti PrakashiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Carlas SmithORCID

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2 papers

Recent·Most cited

BioinformaticsForensic Science InternationalPeer-reviewedDec 2, 2025

Reducing Manual Labour in Forensic Microtrace Recognition with Deep Learning

Gerben Rijpkema, Dylan Kalisvaart, Serafim Korovin…·Carlas Smith

This paper develops a deep learning model to automate microtrace recognition in forensic microscopy, reducing manual annotation needs by 65% through combined pretraining strategies.

1 citation

imagingdeep learning

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BioinformaticsbioRxivPreprintAug 20, 2024

CLEAR-IT: Contrastive Learning to Capture the Immune Composition of Tumor Microenvironments

Daniel Spengler, Serafim Korovin, Kirti Prakash…·Hayri Emrah Balcioglu

CLEAR-IT improves cell phenotyping in tumor microenvironments by using contrastive learning, outperforming existing methods across imaging modalities and requiring fewer labels.

DOI 10.1101/2024.08.20.608738

imagingcontrastive learning

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