Author

Roman David Buelow

Universitätsklinikum Aachen

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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Bioinformatics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Organism:human· 2 Approach:transformer / foundation model· 2 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Jakob Nikolas KatheriD· 3 shared Peter BooriD· 3 shared Amelie EchleiD· 2 shared Bastian DislichiD· 2 shared Chiara Maria Lavinia LoeffleriD· 2 shared Christian TrautweiniD· 2 shared Daniel TruhniD· 2 shared Hannah Sophie MutiiD· 2 shared

Activity by Year

2020-2022 · 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

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3 papers

Recent·Most cited

Adversarial attacks and adversarial robustness in computational pathology

Narmin Ghaffari Laleh, Daniel Truhn, Gregory Patrick Veldhuizen, Tianyu Han, Marko van Treeck, Roman David Buelow, Rupert Langer, Bastian Dislich, Peter Boor, Volkmar Schulz, Jakob Nikolas Kather

This paper evaluates adversarial robustness of CNNs and ViTs in computational pathology, finding ViTs are more robust to attacks.

bioRxiv · Mar 15, 2022 · PreprintBioinformatics

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

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather, Lara Rosaline Heij, Heike Irmgard Grabsch, Chiara Maria Lavinia Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan Niehues, Kai A. J. Sommer, Peter Bankhead, Loes F. S. Kooreman, Jefree J. Schulte, Nicole A. Cipriani, Roman David Buelow, Peter Boor, , Tom Luedde

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.

Nature Cancer · Jul 27, 2020Peer-reviewedGenetics