314 citations · 490 across the 9 of their papers we have counts for
10 papers
Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification
Marta Oliveira, Rick Wilming, Benedict Clark +4
Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improve…
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
Fabian Eitel, Marc-André Schulz, Moritz Seiler +2
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful to…
Feature visualization for convolutional neural network models trained on neuroimaging data
Fabian Eitel, Anna Melkonyan, Kerstin Ritter
A major prerequisite for the application of machine learning models in clinical decision making is trust and interpretability. Current explainability studies in the neuroimaging co…
Evaluating saliency methods on artificial data with different background types
Céline Budding, Fabian Eitel, Kerstin Ritter +1
Over the last years, many 'explainable artificial intelligence' (xAI) approaches have been developed, but these have not always been objectively evaluated. To evaluate the quality…
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs
Fabian Eitel, Jan Philipp Albrecht, Martin Weygandt +2
Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatia…
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
Fabian Eitel, Jan Philipp Albrecht, Friedemann Paul +1
Neuroimaging studies based on magnetic resonance imaging (MRI) typically employ rigorous forms of preprocessing. Images are spatially normalized to a standard template using linear…