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arXiv · 2107.05990

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

Sebastian Pölsterl·Tom Nuno Wolf·Christian Wachinger
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Abstract

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.

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Sebastian Pölsterl, Tom Nuno Wolf, Christian Wachinger. 2021-07-13. Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform. https://doi.org/10.1007/978-3-030-87240-3_66

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