  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.
