Christoph M. Friedrich at University of Applied Sciences and Arts Dortmund - Research Interests, Publications & Academic Profile

Christoph M. Friedrich

Professor of Biomedical Computer Science

Technische Universität Dortmund

Germany

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Research Interests

Cognitive Neuroscience

20%

Neuropsychology

30%

Neuroinformatics

40%

Machine Learning

30%

Alzheimer's Disease

30%

Neurodegenerative Disease

20%

Disease Classification

20%

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Articles14

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

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Year:

2024

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

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Year:

2024

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

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Year:

2023

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

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Year:

2023

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

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Year:

2023

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

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Year:

2023

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

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Year:

2023

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

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Year:

2023

Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

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Year:

2022

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

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2022

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

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Year:

2022

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

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Year:

2021

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

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Year:

2020

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

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Year:

2020

Collaborators11

Reinhard Furrer

University of Zurich

SWITZERLAND

Aki Laakso

University of Helsinki

FINLAND

Antti E. Lindgren

Kuopio University Hospital

FINLAND

Philippe Bijlenga

Professeur associé

Université Grenoble Alpes Faculté de Médecine

SWITZERLAND

Vasile-Dan Hodoroaba

Federal Institute For Materials Research and Testing

GERMANY

Nadja Bauer

Professor for Data Science, Statistics

Technische Universität Dortmund

GERMANY

Behnam Rezai Jahromi

University of Helsinki

FINLAND

Elisa Cuadrado-Godia

Assistant Profesor

Universitat Pompeu Fabra - Campus del Mar

SPAIN

Martha Jokisch

University Hospital Essen

GERMANY

Joanna Pera

Prof.

Uniwersytet Jagiellonski Collegium Medicum

POLAND

Sven Hirsch

University of Zurich

SWITZERLAND

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