Kerstin Ritter geb. Hackmack
Kerstin Ritter geb. Hackmack
Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin
Verified email at bccn-berlin.de - Homepage
Title
Cited by
Cited by
Year
The role of neural impulse control mechanisms for dietary success in obesity
M Weygandt, K Mai, E Dommes, V Leupelt, K Hackmack, T Kahnt, ...
Neuroimage 83, 669-678, 2013
1192013
Impulse control in the dorsolateral prefrontal cortex counteracts post-diet weight regain in obesity
M Weygandt, K Mai, E Dommes, K Ritter, V Leupelt, J Spranger, ...
Neuroimage 109, 318-327, 2015
872015
Multi-scale classification of disease using structural MRI and wavelet transform
K Hackmack, F Paul, M Weygandt, C Allefeld, JD Haynes, ...
Neuroimage 62 (1), 48-58, 2012
702012
Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification
M Böhle, F Eitel, M Weygandt, K Ritter
Frontiers in aging neuroscience 11, 194, 2019
632019
Can we overcome the ‘clinico-radiological paradox’in multiple sclerosis?
K Hackmack, M Weygandt, J Wuerfel, CF Pfueller, J Bellmann-Strobl, ...
Journal of neurology 259 (10), 2151-2160, 2012
632012
Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers
K Ritter, J Schumacher, M Weygandt, R Buchert, C Allefeld, JD Haynes, ...
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 1 (2 …, 2015
592015
fMRI pattern recognition in obsessive–compulsive disorder
M Weygandt, CR Blecker, A Schäfer, K Hackmack, JD Haynes, D Vaitl, ...
Neuroimage 60 (2), 1186-1193, 2012
522012
MRI pattern recognition in multiple sclerosis normal-appearing brain areas
M Weygandt, K Hackmack, C Pfüller, J Bellmann–Strobl, F Paul, F Zipp, ...
PloS one 6 (6), e21138, 2011
452011
Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease
J Rieke, F Eitel, M Weygandt, JD Haynes, K Ritter
Understanding and Interpreting Machine Learning in Medical Image Computing …, 2018
432018
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
F Eitel, E Soehler, J Bellmann-Strobl, AU Brandt, K Ruprecht, RM Giess, ...
NeuroImage: Clinical 24, 102003, 2019
402019
Insulin action in the brain regulates mitochondrial stress responses and reduces diet-induced weight gain
K Wardelmann, S Blümel, M Rath, E Alfine, C Chudoba, M Schell, W Cai, ...
Molecular metabolism 21, 68-81, 2019
322019
Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification
F Eitel, K Ritter, Alzheimer’s Disease Neuroimaging Initiative (ADNI
Interpretability of Machine Intelligence in Medical Image Computing and …, 2019
232019
Stress-induced brain activity, brain atrophy, and clinical disability in multiple sclerosis
M Weygandt, L Meyer-Arndt, JR Behrens, K Wakonig, J Bellmann-Strobl, ...
Proceedings of the National Academy of Sciences 113 (47), 13444-13449, 2016
172016
Mental speed is associated with the shape irregularity of white matter MRI hyperintensity load
C Lange, P Suppa, A Mäurer, K Ritter, U Pietrzyk, E Steinhagen-Thiessen, ...
Brain imaging and behavior 11 (6), 1720-1730, 2017
92017
Combination of structural MRI and FDG-PET of the brain improves diagnostic accuracy in newly manifested cognitive impairment in geriatric inpatients
K Ritter, C Lange, M Weygandt, A Mäurer, A Roberts, M Estrella, P Suppa, ...
Journal of Alzheimer's Disease 54 (4), 1319-1331, 2016
72016
Multivariate decoding of fMRI data
J Heinzle, S Anders, S Bode, C Bogler, Y Chen, RM Cichy, K Hackmack, ...
e-Neuroforum 18 (1), 1-16, 2012
72012
MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis
M Weygandt, HM Hummel, K Schregel, K Ritter, C Allefeld, E Dommes, ...
NeuroImage: Clinical 7, 400-408, 2015
62015
COVID-19: A simple statistical model for predicting intensive care unit load in exponential phases of the disease
M Ritter, DVM Ott, F Paul, JD Haynes, K Ritter
Scientific Reports 11 (1), 1-12, 2021
52021
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
F Eitel, JP Albrecht, F Paul, K Ritter
arXiv preprint arXiv:1911.06278, 2019
32019
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
F Eitel, MA Schulz, M Seiler, H Walter, K Ritter
Experimental Neurology, 113608, 2021
22021
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