Roman David Bülow

Professor (W1) for Digital Pathology

RWTH Aachen University

Germany

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Professor Roman David Bülow holds the position of Professor (W1) for Digital Pathology at RWTH Aachen University in Germany. His research focuses on computational pathology, quantitative nephropathology, and the integration of omics technologies. Recent articles contributed by Professor Bülow explore advancements in digital pathology, deep learning applications for kidney histology, and the development of tools for data analysis in the field.

Articles (19)

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

Collaborators (12)

Patrick Droste

RWTH Aachen University

GERMANY

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Peter J. Wild

University Hospital Frankfurt

GERMANY

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Sven H. Loosen

University Hospital Düsseldorf

GERMANY

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Joachim Weis

Medizinische Fakultät der RWTH Aachen

GERMANY

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Vladimir Tesar

CZECH REPUBLIC

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Saskia von Stillfried

University Hospital RWTH Aachen

GERMANY

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David Laurin Hölscher

RWTH Aachen University

GERMANY

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Peter Boor

University Hospital RWTH Aachen

GERMANY

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Ulysses Balis

University of Michigan

UNITED STATES

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Tobias Seraphin

University Hospital Düsseldorf

GERMANY

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Frank Kirchhoff

Ulm University Medical Center

GERMANY

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Norman Zerbe

Charité - Universitätsmedizin Berlin

GERMANY

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Roman David Bülow

Research Interests

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Articles (19)

DNA-binding protein-A: a multitool in tubular epithelial cells

Entwicklung und Fortschritte des Nationalen Obduktionsnetzwerks (NATON)

Decoding pathology: the role of computational pathology in research and diagnostics

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

Advances in computational quantitative nephropathology

Deep learning applications for kidney histology analysis

Extending the landscape of omics technologies by pathomics

Clinical, Imaging, and Histopathological Features of Pulmonary Sequelae Following Mild COVID-19

Clinical, Imaging, and Histopathological Features of Pulmonary Sequelae after Mild COVID-19

Computational pathology for nephropathology

Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning

Next-Generation Morphometry for pathomics-data mining in histopathology

Stain-Independent Deep Learning–Based Analysis of Digital Kidney Histopathology

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

Intracranial hemorrhage in COVID-19 patients during extracorporeal membrane oxygenation for acute respiratory failure: a nationwide register study report

Publisher Correction to: Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology

Collaborators (12)

Patrick Droste

Peter J. Wild

Sven H. Loosen

Joachim Weis

Vladimir Tesar

Saskia von Stillfried

David Laurin Hölscher

Peter Boor

Ulysses Balis

Tobias Seraphin

Frank Kirchhoff

Norman Zerbe

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