Search Results for author: Kerstin Ritter

Found 14 papers, 7 papers with code

EEG-Language Modeling for Pathology Detection

no code implementations2 Sep 2024 Sam Gijsen, Kerstin Ritter

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.

Contrastive Learning EEG +2

DeepRepViz: Identifying Confounders in Deep Learning Model Predictions

1 code implementation27 Sep 2023 Roshan Prakash Rane, Jihoon Kim, Arjun Umesha, Didem Stark, Marc-André Schulz, Kerstin Ritter

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.

Deep Learning

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

no code implementations20 Jan 2023 Fabian Eitel, Marc-André Schulz, Moritz Seiler, Henrik Walter, Kerstin Ritter

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.

Transfer Learning

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies

no code implementations22 Jul 2022 Di Wang, Nicolas Honnorat, Peter T. Fox, Kerstin Ritter, Simon B. Eickhoff, Sudha Seshadri, Mohamad Habes

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

no code implementations24 Mar 2022 Fabian Eitel, Anna Melkonyan, Kerstin Ritter

A major prerequisite for the application of machine learning models in clinical decision making is trust and interpretability.

Classification Decision Making +1

Evaluating saliency methods on artificial data with different background types

1 code implementation9 Dec 2021 Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe

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

no code implementations23 Jul 2020 Fabian Eitel, Jan Philipp Albrecht, Martin Weygandt, Friedemann Paul, Kerstin Ritter

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.

Alzheimer's Disease Detection

Covid-19 -- A simple statistical model for predicting ICU load in early phases of the disease

1 code implementation6 Apr 2020 Matthias Ritter, Derek V. M. Ott, Friedemann Paul, John-Dylan Haynes, Kerstin Ritter

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

no code implementations14 Nov 2019 Fabian Eitel, Jan Philipp Albrecht, Friedemann Paul, Kerstin Ritter

Neuroimaging studies based on magnetic resonance imaging (MRI) typically employ rigorous forms of preprocessing.

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

1 code implementation18 Apr 2019 Fabian Eitel, Emily Soehler, Judith Bellmann-Strobl, Alexander U. Brandt, Klemens Ruprecht, René M. Giess, Joseph Kuchling, Susanna Asseyer, Martin Weygandt, John-Dylan Haynes, Michael Scheel, Friedemann Paul, Kerstin Ritter

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...

Decision Making General Classification +1

Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data

1 code implementation18 Mar 2019 Moritz Böhle, Fabian Eitel, Martin Weygandt, Kerstin Ritter

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

1 code implementation8 Aug 2018 Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter

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.

Decision Making

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