MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Convolutional neural network models have become one of the most commonly used methods for analyzing medical images. Among them, the codec structure has brought important breakthrough results for medical image segmentation. However, the current medical image segmentation method based on the codec network architecture still has many problems. The corresponding feature map of the codec network in the skip connection structure has a large semantic ambiguity, which may increase the difficulty of learning the network and reduce the segmentation performance. The codec network architecture cannot make full use of the relationship between objects in the global view, and also ignores the global context information of different scales. In this article, we add attention gate mechanism (AGs) to the jump connection structure, and introduce attention mechanism and multi-scale mechanism to solve the above problems. Our model obtains better segmentation performance while introducing fewer parameters.