# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
###### Abstract
Examples include Swin-UNet [17], TransDeepLab [18], and MISSFormer [19], which employ Transformers throughout both the encoding and decoding phases. While these methods excel at capturing global features, they often neglect local detail information. In response, some strategies have combined Transformers with CNN-based networks, such as TransUNet [20] and Transception [21], where Transformers are typically appended after the CNN encoding module. Although these hybrid approaches merge the strengths of CNNs and Transformers, they struggle to effectively integrate the features globally extracted by the Transformers with the local details captured by the CNNs, thereby compromising their segmentation capabilities. In contrast, other methods, like TransFuse [22], CTC-Net [23], and HiFormer [24], link CNNs and Transformers in parallel to concurrently extract global and local features. However, these methods frequently rely on simplistic feature fusion and lack interactive learning between the two encoder types, which prevents them from fully exploiting the complementary benefits of CNNs and Transformers. Given the limitations inherent in existing hybrid architectures, which struggle to effectively combine the local detail extraction capabilities of CNNs with the global attention capabilities of Transformers, this study introduces a dual-encoder interactive learning network designed specifically for medical image segmentation. This network employs both Transformers and CNNs concurrently to extract image features, wherein the Transformers focus on global feature extraction and the CNNs concentrate on capturing local detail information. To better leverage the strengths of both architectures, we introduce the global-local interaction learning (GLIL) module, which facilitates interaction between the Transformer and CNN encoders. Additionally, a global-local feature fusion (GLFF) module is utilized to integrate features from both encoders, thereby enhancing the fusion process and achieving a more accurate feature representation. Extensive experiments were conducted on the Kvasir-SEG dataset [25], the ACDC cardiac dataset [26], and the Synapse multi-organ dataset [27] for validating the proposed method's effectiveness. The contributions of this study are presented below:  
* The introduction of a novel network, DEFI-Net, notably enhancing the MIS performance through Transformer-CNN interactive learning. This interaction is facilitated by the GLIL module. The GLIL module consists of two parallel spatial attention modules: one module extracts global semantic information from the features of the Transformer encoder while the other captures local detail information from the features of the CNN encoder. Effective interaction between the two encoders is achieved by using the global semantic information to weight the features from the CNN encoder and refining the features from the Transformer encoder with local detail information. * The development of a GLFF module that utilizes the global semantic information and local detail information produced by the GLIL module to integrate features from both the Transformer and CNN encoders. The GLFF module concatenates the features from the two encoders and employs a channel attention mechanism to enhance relevant channel features while suppressing less significant ones. The global semantic and local detail information from the GLIL module is then used to weight the concatenated features, resulting in a more precise feature representation. * An evaluation of the robustness and generalizability of the proposed network through extensive experiments on three distinct medical image datasets: the Synapse multi-organ dataset, the ACDC cardiac dataset, and the Kvasir-SEG dataset. The experimental results demonstrate significant improvements over several existing MIS methods, thus further validating the effectiveness of the proposed approach.