Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke
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.
BrainLes@MICCAI