Author

Oliver Lester Saldanha

Universitätsklinikum AachenORCID 0000-0002-3594-7590Unclaimed 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· 3 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Jakob Nikolas KatheriD· 3 shared Christian TrautweiniD· 2 shared Daniel TruhniD· 2 shared Elizabeth AlwersiD· 2 shared Firas KhaderiD· 2 shared Hannah Sophie MutiiD· 2 shared Heike Irmgard GrabschiD· 2 shared Hermann BrenneriD· 2 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Jakob Nikolas KatherORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Oliver Lester Saldanha, Vivien Goepp, Kevin Pfeiffer, Hyojin Kim, Jie Fu Zhu, Rafael Kramann, Sikander Hayat, Jakob Nikolas Kather

SwarmMAP uses swarm learning to automate cell-type annotation in single-cell data without sharing raw data, achieving high accuracy across human tissues.

npj Systems Biology and Applications · Feb 18, 2026Peer-reviewedBioinformatics

Swarm learning for decentralized artificial intelligence in cancer histopathology

Oliver Lester Saldanha, Philip Quirke, Nicholas Paul West, Jacqueline A. James, Maurice Bernard Loughrey, Heike Irmgard Grabsch, Manuel Salto‐Tellez, Elizabeth Alwers, Didem Çifçi, Narmin Ghaffari Laleh, Tobias Seibel, Richard G. Gray, Gordon G. A. Hutchins, Hermann Brenner, Marko van Treeck, , Jakob Nikolas Kather

This paper demonstrates swarm learning for training AI models on decentralized histopathology data to predict molecular features in colorectal cancer.

Nature Medicine · Apr 25, 2022Peer-reviewedBioinformatics

Benchmarking artificial intelligence methods for end-to-end computational pathology

Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y. Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, Roman David Buelow, Heike Irmgard Grabsch, Hermann Brenner, Jenny C. Chang-Claude, Elizabeth Alwers, , Jakob Nikolas Kather

This study benchmarks AI methods for end-to-end computational pathology, showing ResNet and ViT outperform MIL and CLAM in mutation prediction but not in tumor subtyping.

bioRxiv · Aug 9, 2021 · PreprintBioinformatics