Abstract
We propose an ensemble of deep neural networks for the two tasks of automated prognosis of post-treatment ischemic stroke, as imposed by the ISLES 2016 Challenge. For lesion outcome prediction, we employ an ensemble of three-dimensional multiscale residual U-Net and a fully convolutional network, trained using image patches. In order to handle class imbalance, we devise a multi-step training strategy. For clinical outcome prediction, we combine a convolutional neural network (CNN) and a logistic regression model. To overcome the small sample size and the need for whole brain image, we use the CNN trained using patches as a feature extractor and trained a shallow network based on the extracted features. Our ensemble approach demonstrated an appealing performance on both problems, and is ranked among the top entries in the Challenge.
Y. Choi, Y. Kwon and H. Lee—Contributed equally.
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References
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Acknowledgments
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP, No. 2016012002). Joong-Ho Won’s research was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP, Nos. 2013R1A1A1057949 and 2014R1A4A1007895).
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Choi, Y., Kwon, Y., Lee, H., Kim, B.J., Paik, M.C., Won, JH. (2016). Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_22
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