BraTS Challenge Manuscripts
نویسندگان
چکیده
Deep Neural Networks (DNNs) are often successful in problems needing to extract information from complexe, high-dimensional inputs, for which useful features are not obvious to design. This paper presents our work on applying DNNs to brain tumor segmentation for the BRATS challenge. We are currently experimenting with several several DNN architectures, leveraging the recent advances in the field such as convolutional layers, max pooling, Maxout units and Dropout regularization. We present preliminary results, for our best performing network on the BRATS2013 training set, leaderboard dataset and challenge dataset. The results are obtained from the evaluation tool available on the Virtual Skeleton database. While we do not beat the best results of BRATS2013 participants with our current architecture, our results are promising.
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