Abstract
Various deep convolutional neural networks (CNNs) have been applied in the task of medical image segmentation. A lot of CNNs have been proved to get better performance than the traditional algorithms. Deep residual network (ResNet) has drastically improved the performance by a trainable deep structure. In this paper, we proposed a new end-to-end network based on ResNet and U-Net. Our CNN effectively combine the features from shallow and deep layers through multi-path information confusion. In order to exploit global context features and enlarge receptive field in deep layer without losing resolution, We designed a new structure called pyramid dilated convolution. Different from traditional networks of CNNs, our network replaces the pooling layer with convolutional layer which can reduce information loss to some extent. We also introduce the LeakyReLU instead of ReLU along the downsampling path to increase the expressiveness of our model. Experiment shows that our proposed method can successfully extract features for medical image segmentation.
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Acknowledgments
This research is partly supported by NSFC (No: 61375048).
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Zhang, Q., Cui, Z., Niu, X., Geng, S., Qiao, Y. (2017). Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_38
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DOI: https://doi.org/10.1007/978-3-319-70096-0_38
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