论文标题
DRU-NET：有效的深卷卷卷神经网络，用于医学图像分割
DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation
论文作者
论文摘要
剩余网络（RESNET）和密集连接的网络（Densenet）显着提高了主要用于对象分类任务的深卷卷神经网络（DCNN）的训练效率和性能。在本文中，我们通过考虑两个网络的优势来提出有效的网络体系结构。所提出的方法集成到用于医学图像分割的编码器dcoder DCNN模型中。我们的方法增加了与RESNET相比的其他跳过连接，但使用的模型参数明显少于Densenet。我们在公共数据集（ISIC 2018 Grand-Brand-Brand-Brand-gallenge）上评估了针对皮肤病变细分和当地大脑MRI数据集的方法。与基于基于RESNET的基于RESNET，基于Densenet的基于RESNET的基于Densenet和注意力网络（ATTNNET）在相同的编码器 - 码头网络结构中的方法相比，我们的方法达到的分割精度明显更高，模型参数数量少于Densenet和Attnnet。该代码可在github上获得（github链接：https：//github.com/minajf/dru-net）。
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).
