Author

Maxim Lippeveld

Ghent UniversityORCID 0000-0002-9527-7162Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Andrew J. FilbyiD· 2 shared Daniel PeraltaiD· 2 shared Yvan SaeysiD· 2 shared Andrew Fuller· 1 shared Carly Knill· 1 shared Emma Ladlow· 1 shared Louise Jane MichaelisiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Andrew J. FilbyORCID

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2 papers

Recent·Most cited

A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data

Maxim Lippeveld, Daniel Peralta, Andrew J. Filby, Yvan Saeys

The paper introduces SCIP, a scalable image processing pipeline for morphological profiling of image cytometry data, demonstrating its application on three datasets to classify and analyze cell types and drug responses.

bioRxiv · Oct 24, 2022 · PreprintBioinformatics

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Maxim Lippeveld, Carly Knill, Emma Ladlow, Andrew Fuller, Louise Jane Michaelis, Yvan Saeys, Andrew J. Filby, Daniel Peralta

This study compares classical machine learning and deep learning for classifying white blood cells using stain-free imaging flow cytometry, achieving balanced accuracies of 0.776 and 0.697 for white blood cells and 0.866 and 0.867 for eosinophils.

Cytometry Part A · Nov 5, 2019Peer-reviewedBioinformatics