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Abstract #1026

Assessing the effect of registration and model quality using attention gates for brain-age prediction with convolutional neural networks.

Nicola K Dinsdale1, Emma Bluemke2, Mark Jenkinson1, and Ana IL Namburete2
1WIN, University of Oxford, Oxford, United Kingdom, 2IBME, University of Oxford, Oxford, United Kingdom

Nonlinear registration forms a part of standard MRI neuroimaging pipelines but leads to suppression of morphological information. Using attention gates within a convolutional neural network, we explore the effect of the nonlinear registration on age prediction, comparing to linear registration. We show that the network is driven by interpolation effects near the ventricles when trained with nonlinear data, whereas when trained with linear data it considers the whole brain volume. The network may, therefore, be missing cortical changes, limiting the utility of the networks in detecting the early stages of neurological disease.

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