EMAP: Explanation by Minimal Adversarial Perturbation. - Matt Chapman-Rounds; Marc-Andre Schulz; Erik Pazos; Konstantinos Georgatzis (CoRR, 2019)

Clusters in Explanation Space: Inferring disease subtypes from model explanations. - Marc-Andre Schulz; Matt Chapman-Rounds; Manisha Verma; Danilo Bzdok; Konstantinos Georgatzis (CoRR, 2019)

FIMAP: Feature Importance by Minimal Adversarial Perturbation. - Matt Chapman-Rounds; Umang Bhatt; Erik Pazos; Marc-Andre Schulz; Konstantinos Georgatzis (AAAI, 2021)

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction. - Marc-Andre Schulz; Bertrand Thirion; Alexandre Gramfort; Gaël Varoquaux; Danilo Bzdok (CoRR, 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)

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)

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)

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)