research
∙
06/21/2023
Benchmark data to study the influence of pre-training on explanation performance in MR image classification
Convolutional Neural Networks (CNNs) are frequently and successfully use...
share
research
∙
01/20/2023
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment recommen...
research
∙
07/22/2022
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Deep neural networks currently provide the most advanced and accurate ma...
research
∙
03/24/2022
Feature visualization for convolutional neural network models trained on neuroimaging data
A major prerequisite for the application of machine learning models in c...
research
∙
12/09/2021
Evaluating saliency methods on artificial data with different background types
Over the last years, many 'explainable artificial intelligence' (xAI) ap...
research
∙
07/23/2020
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs
Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), ...
research
∙
04/06/2020
Covid-19 – A simple statistical model for predicting ICU load in exponential phases of the disease
One major bottleneck in the ongoing Covid-19 pandemic is the limited num...
research
∙
11/14/2019
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
Neuroimaging studies based on magnetic resonance imaging (MRI) typically...
research
∙
09/19/2019
Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification
Attribution methods are an easy to use tool for investigating and valida...
research
∙
04/18/2019
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Machine learning-based imaging diagnostics has recently reached or even ...
research
∙
08/08/2018