  Residual network (ResNet) and densely connected network (DenseNet) have
significantly improved the training efficiency and performance of deep
convolutional neural networks (DCNNs) mainly for object classification tasks.
In this paper, we propose an efficient network architecture by considering
advantages of both networks. The proposed method is integrated into an
encoder-decoder DCNN model for medical image segmentation. Our method adds
additional skip connections compared to ResNet but uses significantly fewer
model parameters than DenseNet. We evaluate the proposed method on a public
dataset (ISIC 2018 grand-challenge) for skin lesion segmentation and a local
brain MRI dataset. In comparison with ResNet-based, DenseNet-based and
attention network (AttnNet) based methods within the same encoder-decoder
network structure, our method achieves significantly higher segmentation
accuracy with fewer number of model parameters than DenseNet and AttnNet. The
code is available on GitHub (GitHub link: https://github.com/MinaJf/DRU-net).
