314 citations · 480 across the 10 of their papers we have counts for
11 papers
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…
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Di Wang, Nicolas Honnorat, Peter T. Fox +4
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and h…
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…
Covid-19 -- A simple statistical model for predicting ICU load in early phases of the disease
Matthias Ritter, Derek V. M. Ott, Friedemann Paul +2
One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patien…