###### Abstract  
The U-net network, with its simple and powerful encoder-decoder structure, dominates the field of medical image segmentation. However, convolution operations are limited by receptive fields. They do not have the ability to model long-range dependencies, but Transformer has the capability of long-term modeling thanks to its core self-attention mechanism, which has been widely applied in the field of medical image segmentation. However, both CNNs and Transformer can only perform correlation calculations for a single sample, ignoring the correlation between different samples. To address these problems, we propose a new Transformer, which we call the Dual-Attention Transformer (DAT). This module captures correlations within a single sample while also learning correlations between different samples. The current U-net and some of its variant models have the problem of inadequate feature fusion, so we also improve the skip connection to strengthen the association between feature maps at different scales, reduce the semantic gap between the encoder and decoder, and further improve the segmentation performance. We refer to this structure as DATUnet. We conducted extensive experiments on the Synapse and ACDC datasets to validate the superior performance of our network, and we achieved an average DSC (%) of 83.6 and 90.9 and an average HD95 of 13.99 and 1.466 for the Synapse and ACDC datasets, respectively. medical image segmentation; Transformer; convolutional neural network +
Footnote †: journal: Medical Image Segmentation  
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Footnote 1: email:  
of U-net [13], researchers have further developed many similar variant models, such as TransUnet [15], TransUnet++ [16], Swin-Unet [17], 3 DUnet [18], Unetr [19], etc. Each of these models has its own characteristics and shows good results on different datasets. However, a convolutional neural network (CNN) [14] may have some limitations when it comes to processing medical images. Because CNNs are based on local perception and feature extraction, they may be limited when processing medical images with a large range of contextual information. This can lead to inaccuracies or missing details in the segmentation results. A CNN's limited capacity to compute correlations for a single sample and its inability to capture long-range relationships prevent future model performance improvement. Additionally, because the features in the encoder and decoder stages are incompatible, it is important to investigate an efficient feature fusion technique in medical image segmentation networks. When feature maps of different scales are simply fused, the segmentation's accuracy may suffer. Recently, the revolutionary enhancements brought by Vision Transformer [20] to the field of computer vision have attracted a lot of attention from researchers, and an increasing number of researchers have devoted themselves to it [21; 22; 23; 24]. Transformer, because of its core self-attention mechanism, is able to model potential correlations between global elements and possesses good context modeling capabilities, which precisely compensates for the disadvantage of a CNN's lack of dependence on capturing long distances. Some efforts have attempted to apply Transformer to medical image segmentation [24; 25; 26], such as TransUnet [15], proposed by [PERSON] et al, which combines a CNN and Transformer and improves the disadvantage of CNN-based networks with poor global modeling capabilities through Transformer. There are also pure Transformer-based structures, such as Swin-Unet [17], which take the form of a sliding window computation that restricts attention to a single window, effectively reducing the computational effort. These networks were proposed to some extent to solve the problem of the poor global modeling capability of CNNs, and some good results were achieved. The Transformer model can be limited in handling long-distance dependencies.