Marc-Andre Schulz
RWTH Aachen University
23 Papers
10 Citations
Marc-Andre Schulz is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 13 publications. Previous affiliations of Marc-Andre Schulz include Humboldt University of Berlin & Charité.
Chat about Author
Papers
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets.
Marc-Andre Schulz,B.T. Thomas Yeo,Joshua T. Vogelstein,Janaina Mourao-Miranada,Jakob Nikolas Kather,Jakob Nikolas Kather,Konrad P. Kording,Blake A. Richards,Danilo Bzdok +8 more
TL;DR: This work systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references to benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets.
Analysing Humanly Generated Random Number Sequences: A Pattern-Based Approach
TL;DR: A pattern-based analysis using the Levenshtein-Damarau distance is both able to predict humanly generate random number sequences and to identify person-specific information within a humanly generated random number sequence.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.
TL;DR: In this article, the authors reviewed recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuro-imaging biomarkers.
39
Performance reserves in brain-imaging-based phenotype prediction
TL;DR: In this article , the authors systematically assess the effect of sample size on prediction performance using sample sizes far beyond what is possible in common neuroimaging studies and find that moving from single imaging modalities to multimodal input data can lead to further improvements in prediction performance, often on par with doubling the sample size.
33
•Proceedings Article
FIMAP: Feature Importance by Minimal Adversarial Perturbation
Matt Chapman-Rounds,Umang Bhatt,Erik Pazos,Marc-Andre Schulz,Konstantinos Georgatzis +4 more
- 18 May 2021
TL;DR: Feature importance by minimal adversarial perturbation (FIMAP) as mentioned in this paper combines the two paradigms, recovering the output of feature-weighting methods in continuous feature spaces, whilst indicating the direction in which the nearest counterfactuals can be found.
11