Author

M. van Rijthoven

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

F. Ciompi· 1 shared F. Pagliarulo· 1 shared H. Moch· 1 shared J. van der Laak· 1 shared K. Silina· 1 shared M. van den Broek· 1 shared P. Schraml· 1 shared S. Obahor· 1 shared

Activity by Year

2023-2023 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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1 shared paperPathology

F. Ciompi

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1 paper

Recent·Most cited

PathologybioRxivApr 3, 2023

Multi-resolution deep learning characterizestertiary lymphoid structures in solid tumors

M. van Rijthoven, S. Obahor, F. Pagliarulo…·K. Silina

This paper introduces HookNet-TLS, a deep learning model for automated TLS quantification in cancer pathology slides, achieving human-level performance and prognostic associations.

DOI 10.1038/s43856-023-00421-7

humanimaging

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