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Marc-Andre Schulz

Marc-André Schulz

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2020 – today

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2024
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  - conf/edbt/SchulzAY0KLR24
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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
2023
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  - journals/corr/abs-2301-08525
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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)
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  - journals/corr/abs-2309-15551
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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
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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]
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  - conf/aaai/Chapman-RoundsB21
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Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, Konstantinos Georgatzis:
FIMAP: Feature Importance by Minimal Adversarial Perturbation. AAAI 2021: 11433-11441
[i3]
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  - journals/corr/abs-2110-06135
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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

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2019
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  - journals/corr/abs-1912-00872
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Matt Chapman-Rounds, Marc-Andre Schulz, Erik Pazos, Konstantinos Georgatzis:
EMAP: Explanation by Minimal Adversarial Perturbation. CoRR abs/1912.00872 (2019)
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  - journals/corr/abs-1912-08755
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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)

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