## 6 Conclusions  
In this paper, we propose a new medical image segmentation model that mainly uses our designed DAT module for encoding and decoding while simultaneously considering the remote correlation of individual samples and the correlation between different samples. Our model effectively combines different fine-grained features through channel-cross fusion to improve the segmentation performance. We validated the superiority of our method on the Synapse Abdomen Multi-Organ and ACDC Heart Segmentation datasets, and, through ablation experiments, we confirmed the importance of each module. The experiments showed that our model significantly outperformed the baseline models, demonstrating its effectiveness in improving the segmentation performance. In a future work, we will focus on how to enhance the generalization performance of the network and reduce the amount of computation, so that the network can be more suitable for practical engineering needs.  
Conceptualization, [PERSON]; methodology, J.C.; software, J.C. and S.J.; validation, S.J. and L.W.; formal analysis, [PERSON] and S.J.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C. and L.W.; visualization, J.C. and S.J. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Params(M)** & **DSC\(\downarrow\)** & **HD95\(\downarrow\)** \\ \hline DeepLapv3 + (CNN) [40] & 59.50 & 77.63 & 39.95 \\ Swin-Unet [17] & 27.17 & 79.13 & 21.55 \\ TransUNet [15] & 105.28 & 77.48 & 31.69 \\ LeVit-Unet [46] & 52.17 & 78.53 & 16.84 \\ MISSFormer [44] & 42.5 & 81.96 & 18.20 \\ HiFormer [42] & 25.51 & 80.39 & 14.70 \\ DAE-Former [27] & 48.1 & 82.43 & 17.46 \\ DATUnet & 59.44 & 83.64 & 13.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the number of parameters.  
Figure 10: Diagram of different connection strategies. From left to right: (**a**) sequential fusion, (**b**) simple additive fusion, and (**c**) complex additive fusion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dual-Attention Strategy** & **DSC\(\uparrow\)** & **HD95\(\downarrow\)** \\ \hline Sequential & 83.64 & 13.99 \\ Simple Additive & 80.13 & 26.22 \\ Complex Additive & 82.58 & 17.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different dual-attention strategies on the Synapse dataset.  
**Funding:** Special Funds for the Central Government to Guide Local Science and Technology Development under Grant ZYXD2022C19 and the Scientific and Technological Innovation 2030 Major Project under Grant 2022 ZD0115802.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Synapse and ACDC datasets are openly available at: [[https://www.synapse.org/#iSynapsesyn3193805/wiki/217789](https://www.synapse.org/#iSynapsesyn3193805/wiki/217789)]([https://www.synapse.org/#iSynapsesyn3193805/wiki/217789](https://www.synapse.org/#iSynapsesyn3193805/wiki/217789)) (accessed on 15 April 2023) and [[https://www.creatis.insa-lyon.fr/Challenge/acdc/](https://www.creatis.insa-lyon.fr/Challenge/acdc/)]([https://www.creatis.insa-lyon.fr/Challenge/acdc/](https://www.creatis.insa-lyon.fr/Challenge/acdc/)) (accessed on 25 April 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.