  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 focused on how these models can utilize the usually
low-dimensional tabular information, such as patient demographics or laboratory
measurements. We introduce the Dynamic Affine Feature Map Transform (DAFT), a
general-purpose module for CNNs that dynamically rescales and shifts the
feature maps of a convolutional layer, conditional on a patient's tabular
clinical information. We show that DAFT is highly effective in combining 3D
image and tabular information for diagnosis and time-to-dementia prediction,
where it outperforms competing CNNs with a mean balanced accuracy of 0.622 and
mean c-index of 0.748, respectively. Our extensive ablation study provides
valuable insights into the architectural properties of DAFT. Our implementation
is available at https://github.com/ai-med/DAFT.
