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2020 – today
- 2024
[j5]Marta Oliveira, Rick Wilming, Benedict Clark, Céline Budding, Fabian Eitel, Kerstin Ritter
, Stefan Haufe:
Benchmarking the influence of pre-training on explanation performance in MR image classification. Frontiers Artif. Intell. 7 (2024)- [c7]Marc-Andre Schulz, Jan Philipp Albrecht, Alpay Yilmaz, Alexander Koch, Dagmar Kainmüller, Ulf Leser, Kerstin Ritter:
TLIMB - A Transfer Learning Framework for IMage Analysis of the Brain. EDBT/ICDT Workshops 2024 - [c6]Roshan Prakash Rane, JiHoon Kim, Arjun Umesha, Didem Stark, Marc-André Schulz, Kerstin Ritter:
DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions. MICCAI (10) 2024: 186-196 - [i12]Sam Gijsen, Kerstin Ritter:
EEG-Language Modeling for Pathology Detection. CoRR abs/2409.07480 (2024) - 2023
- [j4]Di Wang, Nicolas Honnorat, Peter T. Fox, Kerstin Ritter, Simon B. Eickhoff, Sudha Seshadri, Mohamad Habes:
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies. NeuroImage 269: 119929 (2023) - [i11]Fabian Eitel, Marc-André Schulz, Moritz Seiler, Henrik Walter, Kerstin Ritter:
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. CoRR abs/2301.08525 (2023) - [i10]Marta Oliveira, Rick Wilming, Benedict Clark, Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe:
Benchmark data to study the influence of pre-training on explanation performance in MR image classification. CoRR abs/2306.12150 (2023) - [i9]Roshan Prakash Rane, JiHoon Kim, Arjun Umesha, Didem Stark, Marc-André Schulz, Kerstin Ritter:
Identifying confounders in deep-learning-based model predictions using DeepRepViz. CoRR abs/2309.15551 (2023) - 2022
- [j3]Pooja Subramaniam, Tabea Kossen, Kerstin Ritter, Anja Hennemuth, Kristian Hildebrand, Adam Hilbert, Jan Sobesky, Michelle Livne, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, Dietmar Frey, Vince I. Madai:
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks. Medical Image Anal. 78: 102396 (2022) - [c5]Marc-Andre Schulz, Alexander Koch, Vanessa Emanuela Guarino, Dagmar Kainmueller, Kerstin Ritter:
Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction. MLCN@MICCAI 2022: 169-178 - [i8]Fabian Eitel, Anna Melkonyan, Kerstin Ritter:
Feature visualization for convolutional neural network models trained on neuroimaging data. CoRR abs/2203.13120 (2022) - [i7]Di Wang, Nicolas Honnorat, Peter T. Fox, Kerstin Ritter, Simon B. Eickhoff, Sudha Seshadri, Mohamad Habes:
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies. CoRR abs/2207.11352 (2022) - 2021
- [j2]Ulf Leser, Marcus Hilbrich, Claudia Draxl, Peter Eisert, Lars Grunske, Patrick Hostert, Dagmar Kainmüller, Odej Kao, Birte Kehr, Timo Kehrer, Christoph Koch, Volker Markl, Henning Meyerhenke, Tilmann Rabl, Alexander Reinefeld, Knut Reinert, Kerstin Ritter, Björn Scheuermann, Florian Schintke, Nicole Schweikardt, Matthias Weidlich:
The Collaborative Research Center FONDA. Datenbank-Spektrum 21(3): 255-260 (2021) - [c4]Malte Klingenberg, Didem Stark, Fabian Eitel, Kerstin Ritter:
MRI Image Registration Considerably Improves CNN-Based Disease Classification. MLCN@MICCAI 2021: 44-52 - [i6]Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe:
Evaluating saliency methods on artificial data with different background types. CoRR abs/2112.04882 (2021) - 2020
- [i5]Fabian Eitel, Jan Philipp Albrecht, Martin Weygandt, Friedemann Paul, Kerstin Ritter:
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs. CoRR abs/2007.11899 (2020)
2010 – 2019
- 2019
- [c3]Fabian Eitel, Kerstin Ritter:
Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer's Disease Classification. iMIMIC/ML-CDS@MICCAI 2019: 3-11 - [c2]Shikhar Srivastava, Fabian Eitel, Kerstin Ritter:
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach. ABCD-NP@MICCAI 2019: 74-82 - [e1]Luping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel A. Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang:
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. Lecture Notes in Computer Science 11796, Springer 2019, ISBN 978-3-030-32694-4 [contents] - [i4]Fabian Eitel, Emily Soehler, Judith Bellmann-Strobl, Alexander Ulrich Brandt, Klemens Ruprecht, René M. Giess, Joseph Kuchling, Susanna Asseyer, Martin Weygandt, John-Dylan Haynes, Michael Scheel, Friedemann Paul, Kerstin Ritter:
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. CoRR abs/1904.08771 (2019) - [i3]Fabian Eitel, Kerstin Ritter:
Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification. CoRR abs/1909.08856 (2019) - [i2]Fabian Eitel, Jan Philipp Albrecht, Friedemann Paul, Kerstin Ritter:
Harnessing spatial MRI normalization: patch individual filter layers for CNNs. CoRR abs/1911.06278 (2019) - 2018
- [c1]Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter:
Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease. MLCN/DLF/iMIMIC@MICCAI 2018: 24-31 - [i1]Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter:
Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease. CoRR abs/1808.02874 (2018) - 2015
- [j1]Martin Weygandt, Knut Mai, Esther Dommes, Kerstin Ritter, Verena Leupelt, Joachim Spranger, John-Dylan Haynes:
Impulse control in the dorsolateral prefrontal cortex counteracts post-diet weight regain in obesity. NeuroImage 109: 318-327 (2015)
Coauthor Index
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