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 …
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 …
Image segmentation enables to extract quantitative measures from scans that can
serve as imaging biomarkers for diseases. However, segmentation quality can vary
substantially across scans, and therefore yield unfaithful estimates in the
follow-up …
Recent methods for generating novel molecules use graph representations of
molecules and employ various forms of graph convolutional neural networks for
inference. However, training requires solving an expensive graph isomorphism
problem, which …
Deep neural networks enable highly accurate image segmentation, but require
large amounts of manually annotated data for supervised training. Few-shot
learning aims to address this shortcoming by learning a new class from a few
annotated support …
We introduce a wide and deep neural network for prediction of progression
from patients with mild cognitive impairment to Alzheimer's disease.
Information from anatomical shape and tabular clinical data (demographics,
biomarkers) are fused in a …