Abstract
In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.
Abstract (translated)
在这项工作中，我们提出了一种用于脑肿瘤分割的多模态卷积神经网络（CNN）方法。我们研究如何在CNN框架中有效地组合不同的模态。我们将先前用于视频识别问题的各种融合方法适应于脑肿瘤分割问题，并且我们研究它们在记忆和性能方面的效率。我们的实验，这些是在BRATS数据集上进行的，我们得出结论：学习每种模态的单独表示并将它们组合用于脑肿瘤分割可以提高CNN系统的性能。
URL
https://arxiv.org/abs/1809.06191
