Author ORCID Identifier
0000-0003-2288-2406
Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences, Cardiac and Cardiovascular systems
Abstract
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.
DOI
https://doi.org/10.21427/dhbt-q252
Recommended Citation
Zihni E., McGarry B.L, & Kelleher JD. (2021). An analysis of the interpretability of neural networks trained on magnetic resonance imaging for stroke outcome prediction. Proc. Intl. Soc. Mag. Reson. Med, vol. 29, pg. 3503. doi:10.21427/dhbt-q252
Funder
Precise4Q
Included in
Artificial Intelligence and Robotics Commons, Cardiovascular Diseases Commons, Data Science Commons