论文标题
DC-UNET：使用双通道有效CNN重新考虑U-NET体系结构，以进行医学图像分割
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
论文作者
论文摘要
最近，深度学习在计算机视觉领域变得越来越流行。卷积神经网络（CNN）在图像分割区域，尤其是医学图像的突破。在这方面，U-NET是医学图像分割任务的主要方法。 U-NET不仅在分割多模式医学图像方面表现良好，而且在某些艰难情况下。但是，我们发现经典的U-NET体系结构在几个方面都有限制。因此，我们应用了修改：1）设计有效的CNN体系结构以替换编码器和解码器，2）施加残差模块以替换编码器和解码器之间的跳过连接以根据状态U-NET模型进行改进。经过这些修改后，我们设计了一种新颖的体系结构-DC-UNET，作为U-NET体系结构的潜在继任者。我们创建了一个新的有效的CNN体系结构，并基于此CNN构建了DC-UNET。我们已经在三个数据集上评估了我们的模型，该模型与经典U-NET相比，性能的相对提高分别为2.90％，1.49％和11.42％。此外，我们使用了Tanimoto的相似性来代替Jaccard相似性，以进行灰色到灰色的图像比较。
Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images. In this regard, U-Net is the predominant approach to medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some tough cases of them. However, we found that the classical U-Net architecture has limitation in several aspects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder and decoder, 2) applied residual module to replace skip connection between encoder and decoder to improve based on the-state-of-the-art U-Net model. Following these modifications, we designed a novel architecture--DC-UNet, as a potential successor to the U-Net architecture. We created a new effective CNN architecture and build the DC-UNet based on this CNN. We have evaluated our model on three datasets with tough cases and have obtained a relative improvement in performance of 2.90%, 1.49% and 11.42% respectively compared with classical U-Net. In addition, we used the Tanimoto similarity to replace the Jaccard similarity for gray-to-gray image comparisons.
