# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Conclusions  
A hybrid CNN-Transformer architecture named DEFI-Net that exhibits superior segmentation performance in MIS tasks is introduced. This advantage arises from effective interaction and feature fusion between the CNN and Transformer encoders. The interaction within DEFI-Net is facilitated by the GLIL, which allows the CNN to absorb global information from the Transformer and vice versa, enabling the Transformer to acquire local detail knowledge from the CNN. This promotes a robust interaction between the two encoders. Feature fusion is accomplished through the GLFF, which harnesses both global and local information to enhance the integration of features. Evaluations conducted on three distinct datasets underscore the superior performance of DEFI-Net compared to other methods. Additionally, ablation experiments validate the critical role of encoder interactions in achieving high segmentation accuracy. However, the dual-encoder structure of DEFI-Net introduces significant computational overhead. Future work will therefore  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Synapse**} & \multicolumn{2}{c}{**ACDC**} & \multicolumn{2}{c}{**Kvasir SEG**} \\ \cline{2-7}  & **DSC \(\uparrow\)** & **HD95 \(\downarrow\)** & **DSC \(\uparrow\)** & **HD95 \(\downarrow\)** & **DSC \(\uparrow\)** & **IoU \(\uparrow\)** \\ \hline Concat & 80.98 & 24.17 & 88.72 & 1.44 & 85.54 & 78.23 \\ +GILL & 82.64 & 18.45 & 91.83 & 1.11 & 86.50 & 79.16 \\ +CLFF & 81.65 & 19.21 & 91.62 & 1.18 & 86.14 & 78.64 \\ DEIF-Net & 83.29 & 18.15 & 92.02 & 1.08 & 87.45 & 80.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on three datasets.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **DSC (\%) \(\uparrow\)** & **IoU (\%) \(\uparrow\)** \\ \hline V-Net [2] & 80.79 & 68.07 \\ U-Net [1] & 82.99 & 71.01 \\ Att-UNet [3] & 82.85 & 70.45 \\ MultiResNet[33] & 81.34 & 68.66 \\ TransUet [20] & 85.61 & 78.15 \\ SwinU-Net [17] & 85.44 & 78.21 \\ MISSFormer [19] & 75.35 & 62.64 \\ TransCeption [21] & 77.32 & 65.21 \\ HiFormer [24] & 85.96 & 78.26 \\ DEIF-Net & 87.45 & 80.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of the proposed method on the Kvasir-SEG dataset, with the best outcomes highlighted in red and the second best in blue.  
focus on developing a more efficient network structure inspired by DEFI-Net, aiming to maintain high accuracy while reducing computational costs.  
Conceptualization, H.Y., Y.F. and P.Y.; data curation, H.Y., Y.F. and P.Y.; formal analysis, H.Y. and P.Y.; funding acquisition, H.Y.; investigation, H.Y. and P.Y.; methodology, H.Y.; project administration, H.Y.; resources, H.Y.; software, H.Y.; supervision, Y.F.; validation, H.Y.; visualization, H.Y. and P.Y.; writing--original draft, H.Y.; writing--review and editing, H.Y., Y.F. and P.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is made available online at [[https://www.synapse.org/Synapse:syn3193805](https://www.synapse.org/Synapse:syn3193805)]([https://www.synapse.org/Synapse:syn3193805](https://www.synapse.org/Synapse:syn3193805)) (accessed on 10 February 2025), [[https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html)]([https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html)) (accessed on 10 February 2025), [[https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)]([https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)) (accessed on 10 February 2025).  
The authors declare no conflicts of interest.