Fully convolutional attention network for biomedical image segmentation
Fully convolutional attention network for biomedical image segmentation
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DOI：
10.1016/j.artmed.2020.101899
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发表时间：
2020-07-01
影响因子：
7.5
通讯作者：
Lv, Xiaoyi
中科院分区：
文献类型：
--
作者：
Cheng, Junlong;Tian, Shengwei;Lv, Xiaoyi
在本文中，我们嵌入两种类型的注意力模块的扩张完全卷积网络（FCN），以解决生物医学图像分割任务的有效性和准确性。与以往通过多尺度特征融合进行图像分割的工作不同，我们提出了全卷积注意力网络（FCANet）来聚合长距离和短距离的上下文信息。具体来说，我们在具有扩张策略的Res2Net网络中添加了两种类型的注意力模块，空间注意力模块和通道注意力模块。通过空间注意模块对各个位置的特征进行聚合，使得相似的特征在空间大小上相互促进。同时，通道注意模块将特征图的每个通道视为特征检测器，并强调任何两个通道图之间的通道依赖性。最后，对两类注意力模块的输出特征之和进行加权，以保留长距离和短距离的特征信息，进一步提高特征的表示性，使生物医学图像分割更加准确。特别是，我们验证了所提出的注意模块可以无缝连接到任何端到端的网络，以最小的开销。我们在三个公共生物医学图像分割数据集上进行了全面的实验，即，胸部X射线收集，Kaggle 2018数据科学碗和Herlev数据集。实验结果表明，FCANet能够有效地提高生物医学图像的分割效果。源代码模型可在https：//www.example.com上获得
In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image seg-mentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray col-lection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https:// github.com/luhongchun/FCANet