Impulse control in the dorsolateral prefrontal cortex counteracts post-diet weight regain in obesity. - Martin Weygandt; Knut Mai; Esther Dommes; Kerstin Ritter; Verena Leupelt; Joachim Spranger; John-Dylan Haynes (NeuroImage, 2015)

Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease. - Johannes Rieke; Fabian Eitel; Martin Weygandt; John-Dylan Haynes; Kerstin Ritter (MLCN/DLF/iMIMIC@MICCAI, 2018)

Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer's Disease Classification. - Fabian Eitel; Kerstin Ritter (iMIMIC/ML-CDS@MICCAI, 2019)

Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach. - Shikhar Srivastava 002; Fabian Eitel; Kerstin Ritter (ABCD-NP@MICCAI, 2019)

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. - 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; et al. (CoRR, 2019)

Harnessing spatial MRI normalization: patch individual filter layers for CNNs. - Fabian Eitel; Jan Philipp Albrecht; Friedemann Paul; Kerstin Ritter (CoRR, 2019)

Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs. - Fabian Eitel; Jan Philipp Albrecht; Martin Weygandt; Friedemann Paul; Kerstin Ritter (CoRR, 2020)

MRI Image Registration Considerably Improves CNN-Based Disease Classification. - Malte Klingenberg; Didem Stark; Fabian Eitel; Kerstin Ritter (MLCN@MICCAI, 2021)

Evaluating saliency methods on artificial data with different background types. - Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe (CoRR, 2021)

The Collaborative Research Center FONDA. - Ulf Leser; Marcus Hilbrich; Claudia Draxl; Peter Eisert; Lars Grunske; Patrick Hostert; Dagmar Kainmüller; Odej Kao; Birte Kehr; Timo Kehrer; Christoph Koch 001; Volker Markl; et al. (Datenbank-Spektrum, 2021)

Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction. - Marc-Andre Schulz; Alexander Koch 007; Vanessa Emanuela Guarino; Dagmar Kainmueller; Kerstin Ritter (MLCN@MICCAI, 2022)

Feature visualization for convolutional neural network models trained on neuroimaging data. - Fabian Eitel; Anna Melkonyan; Kerstin Ritter (CoRR, 2022)

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks. - 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; et al. (Medical Image Anal., 2022)

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. - Fabian Eitel; Marc-André Schulz; Moritz Seiler; Henrik Walter; Kerstin Ritter (CoRR, 2023)

Benchmark data to study the influence of pre-training on explanation performance in MR image classification. - Marta Oliveira; Rick Wilming; Benedict Clark; Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe (CoRR, 2023)

Identifying confounders in deep-learning-based model predictions using DeepRepViz. - Roshan Prakash Rane; JiHoon Kim; Arjun Umesha; Didem Stark; Marc-André Schulz; Kerstin Ritter (CoRR, 2023)

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies. - Di Wang; Nicolas Honnorat; Peter T. Fox; Kerstin Ritter; Simon B. Eickhoff; Sudha Seshadri; Mohamad Habes (NeuroImage, 2023)

TLIMB - A Transfer Learning Framework for IMage Analysis of the Brain. - Marc-Andre Schulz; Jan Philipp Albrecht; Alpay Yilmaz; Alexander Koch 007; Dagmar Kainmüller; Ulf Leser; Kerstin Ritter (EDBT/ICDT Workshops, 2024)

DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions. - Roshan Prakash Rane; JiHoon Kim; Arjun Umesha; Didem Stark; Marc-André Schulz; Kerstin Ritter (MICCAI (10), 2024)

EEG-Language Modeling for Pathology Detection. - Sam Gijsen; Kerstin Ritter (CoRR, 2024)

Benchmarking the influence of pre-training on explanation performance in MR image classification. - Marta Oliveira; Rick Wilming; Benedict Clark; Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe (Frontiers Artif. Intell., 2024)