Deep learning offers a powerful approach for analyzing hippocampal changes in
Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless,
an input format needs to be selected to pass the image information to the neural
network, …
The reconstruction of cortical surfaces from brain magnetic resonance imaging
(MRI) scans is essential for quantitative analyses of cortical thickness and
sulcal morphology. Although traditional and deep learning-based algorithmic
pipelines exist for …
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Diseasediagnosis use different biomarker combinations to classify patients, but do notallow extracting knowledge about the interactions of biomarkers. However, toimprove our …
The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end,we introduce TransforMesh, a spatio-temporal network based on transformers thatmodels …
Prior work on diagnosing Alzheimer's disease from magnetic resonance images
of the brain established that convolutional neural networks (CNNs) can leverage
the high-dimensional image information for classifying patients. However,
little research …
Deep Neural Networks (DNNs) have an enormous potential to learn from complex
biomedical data. In particular, DNNs have been used to seamlessly fuse
heterogeneous information from neuroanatomy, genetics, biomarkers, and
neuropsychological tests for …
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential models and thereby supports various survival tasks, such as competing …
Studying the relationship between neuroanatomy and cognitive decline due to
Alzheimer's has been a major research focus in the last decade. However, to
infer cause-effect relationships rather than simple associations from
observational data, we need …
Spatial and channel re-calibration have become powerful concepts in computer
vision. Their ability to capture long-range dependencies is especially useful for
those networks that extract local features, such as CNNs. While re-calibration has
been …
The desire to train complex machine learning algorithms and to
increase the statistical power in association studies drives
neuroimaging research to use ever-larger datasets. The most obvious
way to increase sample size is by pooling scans from …