deep-learning

DAFT: A Universal Module to Interweave Tabular Data and 3D Images in CNNs

Prior work on Alzheimer's Disease (AD) has demonstrated that convolutional neural networks (CNNs) can leverage the high-dimensional image information for diagnosing patients. Beside such data-driven approaches, many established biomarkers exist and …

Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs

Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that …

CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis

Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer's disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have …

Hippocampal representations for deep learning on Alzheimer’s disease

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, …

Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks

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 …

Alzheimer’s Disease Diagnosis via Deep Factorization Machine Models

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 …

TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes

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 …

Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform

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 …

Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data

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 …

Semi-Structured Deep Piecewise Exponential Models

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 …