In recent years, U-Net and its variants have been widely used in pathology image segmentation tasks. One of the key designs of U-Net is the use of skip connections between the encoder and decoder, which helps to recover detailed information after upsampling. While most variations of U-Net adopt the original skip connection design, there is semantic gap between the encoder and decoder that can negatively impact model performance. Therefore, it is important to reduce this semantic gap before conducting skip connection. To address this issue, we propose a new segmentation network called FusionU-Net, which is based on U-Net structure and incorporates a fusion module to exchange information between different skip connections to reduce semantic gaps. Unlike the other fusion modules in existing networks, ours is based on a two-round fusion design that fully considers the local relevance between adjacent encoder layer outputs and the need for bi-directional information exchange across multiple layers. We conducted extensive experiments on multiple pathology image datasets to evaluate our model and found that FusionU-Net achieves better performance compared to other competing methods. We argue our fusion module is more effective than the designs of existing networks, and it could be easily embedded into other networks to further enhance the model performance.

通过在U-Net结构中加入融合模块以减小编解码器之间的语义差距，我们提出了一种名为FusionU-Net的新型分割网络，经过在多个病理图像数据集上的广泛实验评估，证明了FusionU-Net相较于其他竞争方法具有更好的性能，我们认为我们的融合模块比现有网络的设计更加有效，且可以轻松嵌入其他网络以进一步提高模型性能。

融合U-Net：增强跳跃连接的病理图像分割U-Net