Christoph M. Friedrich

Professor of Biomedical Computer Science

Technische Universität Dortmund

Germany

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Professor Christoph M. Friedrich is a faculty member in the Department of Biomedical Computer Science at the University of Applied Sciences and Arts Dortmund, Germany. His research focuses on areas including chronic wound segmentation, acoustic event detection in vehicles, and machine learning applications in biomedical contexts. He has contributed to various publications, addressing topics such as melanoma guidelines, Alzheimer’s disease detection, and the analysis of clinical notes.

Articles (19)

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2025

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2020

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2020

Collaborators (12)

Reinhard Furrer

University of Zurich

SWITZERLAND

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Aki Laakso

University of Helsinki

FINLAND

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Antti E. Lindgren

Kuopio University Hospital

FINLAND

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Philippe Bijlenga

Professeur associé

Université Grenoble Alpes Faculté de Médecine

SWITZERLAND

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Vasile-Dan Hodoroaba

Federal Institute For Materials Research and Testing

GERMANY

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Nadja Bauer

Professor for Data Science, Statistics

Technische Universität Dortmund

GERMANY

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Neil D. Reeves

Manchester Metropolitan University

UNITED KINGDOM

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Behnam Rezai Jahromi

University of Helsinki

FINLAND

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Elisa Cuadrado-Godia

Assistant Profesor

Universitat Pompeu Fabra - Campus del Mar

SPAIN

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Martha Jokisch

University Hospital Essen

GERMANY

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Joanna Pera

Prof.

Uniwersytet Jagiellonski Collegium Medicum

POLAND

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Sven Hirsch

University of Zurich

SWITZERLAND

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Christoph M. Friedrich

Research Interests

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Articles (19)

Response to Letter: Re: “Comparative analysis of international melanoma guidelines”

An enhanced harmonic densely connected hybrid transformer network architecture for chronic wound segmentation utilising multi-colour space tensor merging

Conventional Training Integrated with SteamVR Tracking 2.0: Body Stability and Coordination Training Evaluation on ICAROS Pro

Acoustic Event Detection in Vehicles: A Multi-Label Classification Approach

Comparative analysis of international melanoma guidelines

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer’s Disease detection

NanoDefiner Framework and e-Tool Revisited According to the European Commission’s Nanomaterial Definition 2022/C 229/01

Medication event extraction in clinical notes: Contribution of the WisPerMed team to the n2c2 2022 challenge

Static and Dynamic Accuracy and Occlusion Robustness of SteamVR Tracking 2.0 in Multi-Base Station Setups

Domain Adaptation of Transformer-Based Models Using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback

PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints Using Literature Databases

Teaching Machine Learning with Industrial Projects in a Joint Computer Science Master Course: Experiences, Challenges, Perspectives

Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

Machine Learning Workflow to Explain Black-Box Models for Early Alzheimer’s Disease Classification Evaluated for Multiple Datasets

Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems.

Nano or Not Nano? A Structured Approach for Identifying Nanomaterials According to the European Commission’s Definition

Collaborators (12)

Reinhard Furrer

Aki Laakso

Antti E. Lindgren

Philippe Bijlenga

Vasile-Dan Hodoroaba

Nadja Bauer

Neil D. Reeves

Behnam Rezai Jahromi

Elisa Cuadrado-Godia

Martha Jokisch

Joanna Pera

Sven Hirsch

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