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Machine Learning|August 15, 2025
Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated dataFrederik Pahde, Thomas Wiegand, Sebastian Lapuschkin, et al.Journal of Neuroscience Methods|October 18, 2016
Interpretable deep neural networks for single-trial EEG classificationIrene Sturm, Sebastian Lapuschkin, Wojciech Samek, et al.Journal of the Royal Society, Interface|January 13, 2021
Revealing the unique features of each individual's muscle activation signaturesJeroen Aeles, Fabian Horst, Sebastian Lapuschkin, et al.IEEE Transactions on Neural Networks and Learning Systems|August 31, 2016
Evaluating the Visualization of What a Deep Neural Network Has LearnedWojciech Samek, Alexander Binder, Gregoire Montavon, et al.Plos One|January 2, 2026
Software for dataset-wide XAI: From local explanations to global insights with Zennit, CoRelAy, and ViRelAyChristopher J Anders, David Neumann, Wojciech Samek, et al.Scientific Reports|February 22, 2019
Explaining the unique nature of individual gait patterns with deep learningFabian Horst, Sebastian Lapuschkin, Wojciech Samek, et al.Nature Communications|March 13, 2019
Unmasking Clever Hans predictors and assessing what machines really learnSebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, et al.Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz|July 14, 2025
[Artificial intelligence under scrutiny: requirements, quality criteria, and testing tools for medical applications]Jackie Ma, Eva Weicken, Frederik Pahde, et al.Scientific Reports|April 15, 2020
Resolving challenges in deep learning-based analyses of histopathological images using explanation methodsMiriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, et al.International Journal of Epidemiology|May 8, 2022
Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcomeAndreas Rieckmann, Piotr Dworzynski, Leila Arras, et al.Pageof 2