Search Results for author:
Found 14 papers, 7 papers with code
EEG-Language Modeling for Pathology Detection
Compared to models exposed to narrower clinical text information, we find such models to retrieve EEGs based on clinical reports (and vice versa) with substantially higher accuracy.
DeepRepViz: Identifying Confounders in Deep Learning Model Predictions
In conclusion, the DeepRepViz framework provides a systematic approach to test for potential confounders such as age, sex, and imaging artifacts and improves the transparency of DL models for neuroimaging studies.
Benchmark data to study the influence of pre-training on explanation performance in MR image classification
Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging.
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 machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls.
Feature visualization for convolutional neural network models trained on neuroimaging data
A major prerequisite for the application of machine learning models in clinical decision making is trust and interpretability.
Evaluating saliency methods on artificial data with different background types
Over the last years, many 'explainable artificial intelligence' (xAI) approaches have been developed, but these have not always been objectively evaluated.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs
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 spatially normalize the data to a standard template using linear and non-linear transformations.
Covid-19 -- A simple statistical model for predicting ICU load in early phases of the disease
Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated.
Populations and Evolution Applications
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
Neuroimaging studies based on magnetic resonance imaging (MRI) typically employ rigorous forms of preprocessing.
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 validating machine learning models.
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of...
Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data
In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data.
2D Human Pose Estimation
Quantitative Methods
Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease
In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.
