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Abstract #3503

An Analysis of the Interpretability of Neural Networks trained on Magnetic Resonance Imaging for Stroke Outcome Prediction

Esra Zihni1, Bryony McGarry1,2, and John D. Kelleher1,3
1PRECISE4Q, Predictive Modelling in Stroke, Information Communications and Entertainment Institute, Technological University Dublin, Dublin, Ireland, 2School of Psychological Science, University of Bristol, Bristol, United Kingdom, 3ADAPT Research Centre, Technological University Dublin, Dublin, Ireland

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

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