Author

M. van Treeck

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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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Research Focus:variant calling· 1

Frequent Co-Authors

C. M. L. Loeffler· 2 shared D. Cifci· 2 shared J. N. Kather· 2 shared K. J. Hewitt· 2 shared O. L. Saldanha· 2 shared T. P. Seraphin· 2 shared A. Echle· 1 shared A. T. Pearson· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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2 shared papersCancer Biology

C. M. L. Loeffler

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2 papers

Recent·Most cited

Self-supervised deep learning for pan-cancer mutation prediction from histopathology

O. L. Saldanha, C. M. L. Loeffler, J. M. Niehues, M. van Treeck, T. P. Seraphin, K. J. Hewitt, D. Cifci, G. P. Veldhuizen, S. Ramesh, A. T. Pearson, J. N. Kather

This paper develops a self-supervised deep learning pipeline to predict genetic mutations from histopathology images across seven cancer types in two large datasets.

bioRxiv · Sep 15, 2022 · PreprintCancer Biology

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

M. van Treeck, D. Cifci, N. Ghaffari Laleh, O. L. Saldanha, C. M. L. Loeffler, K. J. Hewitt, H. S. Muti, A. Echle, T. Seibel, T. P. Seraphin, C. Trautwein, S. Foersch, T. Luedde, D. Truhn, J. N. Kather

DeepMed provides a modular, scalable AI pipeline for predicting molecular and histopathological features from histopathology images, with public benchmark data.

bioRxiv · Dec 19, 2021 · PreprintCancer Biology