| 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 | 213 | 2019 |
| 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 | 129 | 2019 |
| 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 | 97 | 2018 |
| 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 | 69 | 2019 |
| Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research F Eitel, MA Schulz, M Seiler, H Walter, K Ritter Experimental Neurology 339, 113608, 2021 | 24 | 2021 |
| Harnessing spatial MRI normalization: patch individual filter layers for CNNs F Eitel, JP Albrecht, F Paul, K Ritter arXiv preprint arXiv:1911.06278, 2019 | 6 | 2019 |
| Visualizing evidence for Alzheimer’s disease in deep neural networks trained on structural MRI data M Böhle, F Eitel, M Weygandt, K Ritter arXiv preprint arXiv:1903.07317, 2019 | 6 | 2019 |
| Mri image registration considerably improves CNN-based disease classification M Klingenberg, D Stark, F Eitel, K Ritter, ... Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN …, 2021 | 5 | 2021 |
| Altered coupling of psychological relaxation and regional volume of brain reward areas in multiple sclerosis K Wakonig, F Eitel, K Ritter, S Hetzer, T Schmitz-Hübsch, ... Frontiers in Neurology 11, 568850, 2020 | 5 | 2020 |
| Predicting fluid intelligence in adolescent brain MRI data: An ensemble approach S Srivastava, F Eitel, K Ritter Challenge in Adolescent Brain Cognitive Development Neurocognitive …, 2019 | 5 | 2019 |
| Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data F Eitel, JP Albrecht, M Weygandt, F Paul, K Ritter Scientific reports 11 (1), 24447, 2021 | 4 | 2021 |
| Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity C Chien, M Seiler, F Eitel, T Schmitz-Hübsch, F Paul, K Ritter Multiple Sclerosis Journal–Experimental, Translational and Clinical 8 (3 …, 2022 | 2 | 2022 |
| Feature visualization for convolutional neural network models trained on neuroimaging data F Eitel, A Melkonyan, K Ritter arXiv preprint arXiv:2203.13120, 2022 | 2 | 2022 |
| Evaluating saliency methods on artificial data with different background types C Budding, F Eitel, K Ritter, S Haufe arXiv preprint arXiv:2112.04882, 2021 | 2 | 2021 |
| Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis MA Schulz, S Hetzer, F Eitel, S Asseyer, L Meyer-Arndt, T Schmitz-Hübsch, ... Iscience 26 (9), 2023 | 1* | 2023 |
| Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs F Eitel, JP Albrecht, M Weygandt, F Paul, K Ritter arXiv preprint arXiv:2007.11899, 2020 | 1 | 2020 |
| Limitations of machine learning in psychiatry: Participation in the PAC 2018 depression challenge F Eitel, S Stober, L Waller, L Dorfschmidt, H Walter, K Ritter medRxiv, 19000562, 2019 | 1 | 2019 |
| Benchmark data to study the influence of pre-training on explanation performance in MR image classification M Oliveira, R Wilming, B Clark, C Budding, F Eitel, K Ritter, S Haufe arXiv preprint arXiv:2306.12150, 2023 | | 2023 |
| Higher performance for women than men in MRI-based Alzheimer’s disease detection M Klingenberg, D Stark, F Eitel, C Budding, M Habes, K Ritter, ... Alzheimer's Research & Therapy 15 (1), 84, 2023 | | 2023 |
| Explainable deep learning classifiers for disease detection based on structural brain MRI data F Eitel Humboldt-Universität zu Berlin, 2022 | | 2022 |
Kerstin Ritter geb. HackmackBernstein Center for Computational Neuroscience, Charité-Universitätsmedizin BerlinBestätigte E-Mail-Adresse bei bccn-berlin.de
