论文标题
U-NET使用堆叠的扩张卷积进行医学图像分割
U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
论文作者
论文摘要
本文提出了一种使用堆叠的扩张卷积进行医学图像分割（SDU-NET）的新型U-NET变体。 SDU-NET采用了在编码器和解码器操作中进行修改的Vanilla U-NET的体系结构（操作指示了同一分辨率的特征映射的所有处理）。与在每个编码器/解码器操作中包含两个标准卷积的香草U-NET不同，SDU-NET使用一个标准卷积，然后进行多次扩张的卷积，并连接所有扩张的卷积输出，以作为下一个操作的输入。实验表明，在所有四个经过测试的分割任务中，SDU-NET的表现优于u-net，注意力NET（ATTU-NET）和经常性残留U-NET（R2U-NET），同时使用约40％的Vanilla U-NET参数，17％的ATTU-NET和R2U-NET的15％的参数。
This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same resolution). Unlike vanilla U-Net which incorporates two standard convolutions in each encoder/decoder operation, SDU-Net uses one standard convolution followed by multiple dilated convolutions and concatenates all dilated convolution outputs as input to the next operation. Experiments showed that SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent residual U-Net (R2U-Net) in all four tested segmentation tasks while using parameters around 40% of vanilla U-Net's, 17% of AttU-Net's, and 15% of R2U-Net's.
