most citedHarnessing spatial MRI normalization: patch individual filter layers for CNNs

1 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV20201 cited

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…

q-bio.PE2020

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…

cs.CV20191 cited

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…

eess.IV2019

Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification

Fabian Eitel, Kerstin Ritter

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear…

cs.CV2019

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Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains.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…

q-bio.QM2019

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Moritz Böhle, Fabian Eitel, Martin Weygandt +1

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging…