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 propose an AutoML approach for the prediction of fluid intelligence from
T1-weighted magnetic resonance images. We extracted 122 features from MRI
scans and employed Sequential Model-based Algorithm Configuration to search
for the best prediction …
We study predicting fluid intelligence of 9–10 year old children from
T1-weighted magnetic resonance images. We extract volume and thickness
measurements from MRI scans using FreeSurfer and the SRI24 atlas. We
empirically compare two predictive …
Neuroimaging datasets keep growing in size to address increasingly complex
medical questions. However, even the largest datasets today alone are too
small for training complex machine learning models. A potential solution is
to increase sample size …
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 …