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2020 – today
- 2024
[c4]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- [c3]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 - 2023
- [i5]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) - [i4]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
- [c2]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 - 2021
- [c1]Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, Konstantinos Georgatzis:
FIMAP: Feature Importance by Minimal Adversarial Perturbation. AAAI 2021: 11433-11441 - [i3]Marc-Andre Schulz, Bertrand Thirion, Alexandre Gramfort, Gaël Varoquaux, Danilo Bzdok:
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction. CoRR abs/2110.06135 (2021)
2010 – 2019
- 2019
- [i2]Matt Chapman-Rounds, Marc-Andre Schulz, Erik Pazos, Konstantinos Georgatzis:
EMAP: Explanation by Minimal Adversarial Perturbation. CoRR abs/1912.00872 (2019) - [i1]Marc-Andre Schulz, Matt Chapman-Rounds, Manisha Verma, Danilo Bzdok, Konstantinos Georgatzis:
Clusters in Explanation Space: Inferring disease subtypes from model explanations. CoRR abs/1912.08755 (2019)
Coauthor Index
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