FusionU-Net：具有增强跳跃连接的U-Net用于病理图像分割
FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation
AI总结：
针对U-Net编码器与解码器间的语义鸿沟，提出FusionU-Net，采用两轮融合模块增强跳跃连接，在多个病理图像数据集上取得更优分割性能。
摘要：
近年来，U-Net及其变体已广泛应用于病理图像分割任务。U-Net的关键设计之一是在编码器和解码器之间使用跳跃连接，这有助于在上采样后恢复详细信息。虽然大多数U-Net变体采用原始的跳跃连接设计，但编码器和解码器之间存在语义鸿沟，这可能对模型性能产生负面影响。因此，在进行跳跃连接之前减少这种语义鸿沟非常重要。为了解决这个问题，我们提出了一种新的分割网络，称为FusionU-Net，它基于U-Net结构，并融合了一个融合模块，以在不同跳跃连接之间交换信息，从而减少语义鸿沟。与现有网络中的其他融合模块不同，我们的融合模块基于两轮融合设计，充分考虑了相邻编码器层输出之间的局部相关性以及跨多层双向信息交换的需求。我们在多个病理图像数据集上进行了广泛实验以评估我们的模型，发现FusionU-Net相比其他竞争方法取得了更好的性能。我们认为我们的融合模块比现有网络的设计更有效，并且可以轻松嵌入到其他网络中，以进一步增强模型性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
