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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…
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…
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Fabian Eitel, Emily Soehler, Judith Bellmann-Strobl +10
Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a…
Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease
Johannes Rieke, Fabian Eitel, Martin Weygandt +2
Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D…