Kerstin Ritter (née Hackmack)

Has grant

Junior professor for Computational Neuroscience

Charité - Universitätsmedizin Berlin

Germany

Research Interests

Explore related searches

Professors in Germany Professors in Neuroscience Neuroscience professors in Germany

Contact this professor

ORCID

Google Scholar

Academic Page

About

Kerstin Ritter (née Hackmack) serves as a Junior Professor for Computational Neuroscience at Charité Universitätsmedizin Berlin, Germany. Her research focuses on machine learning applications in clinical settings, particularly regarding neurological diseases such as multiple sclerosis and Alzheimer's disease. Recent publications include studies on prediction models for clinical outcomes and the influence of generative modeling in neuroscience research.

Recent Grants

Grant: Close

DeepMS: Deep Learning for predicting disease activity in MS

Open Date: 2019-11-01

Close Date: 2021-10-01

Grant: Close

Studying mental health via research domain criteria, neuroimaging and convolutional neural networks

Open Date: 2019-01-01

Close Date: 2021-01-01

Grant: Close

Deep learning for neuroimaging-based disease decoding

Open Date: 2017-01-01

Close Date:

Kerstin Ritter (née Hackmack)

Research Interests

Explore related searches

Contact this professor

About

Recent Grants

Articles (17)

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis

Benchmarking the influence of pre-training on explanation performance in MR image classification.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives

Performance reserves in brain-imaging-based phenotype prediction.

Secure federated learning for Alzheimer's disease detection.

Pioneering new paths: the role of generative modelling in neurological disease research.

Artificial Intelligence Bias in Health Care: Web-Based Survey

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis.

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.

Higher performance for women than men in MRI-based Alzheimer's disease detection.

Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.

Structural differences in adolescent brains can predict alcohol misuse.

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks.

Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease.

The Collaborative Research Center FONDA.

COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease

Collaborators (17)

Thomas Liman

Stefan Haufe

David Owald

Eric Artiges

Christiane Montag

Frederik Barkhof

Florian Schlagenhauf

Moritz Seiler

Shuyan Liu

Mira Tschorn

Anja Hennemuth

Jan Sobesky

Ann-Kathrin Stock

Marcus Hilbrich

Matthias Ritter

Sylvia Thun

Menno Schoonheim

How do I reach out?

Meet Kite AI