标题： nnY-Net：用于3D医学图像分割的跨注意力Swin-NeXt 显示英文标题

标题： nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images Segmentation

摘要： 本文提出了一种新的3D医学图像分割模型结构，称为nnY-Net。 这个名称来源于我们的模型在U-net结构底部添加了一个交叉注意力模块，形成了Y结构。 我们结合了两种最新SOTA模型MedNeXt和SwinUNETR的优点，并创新性地设计了Swin-NeXt结构，使用Swin Transformer作为编码器和ConvNeXt作为解码器。 我们的模型将编码器的最低层特征图作为Key和Value，并使用患者特征如病理和治疗信息作为Query，在交叉注意力模块中计算注意力权重。 此外，我们基于dynUnet和nnU-net框架简化了一些3D图像分割的预处理和后处理以及数据增强方法。 我们将提出的带有交叉注意力框架的Swin-NeXt集成到该框架中。 最后，我们构建了一个DiceFocalCELoss来提高对体素分类不均衡数据收敛的训练效率。 显示英文摘要

摘要： This paper provides a novel 3D medical image segmentation model structure called nnY-Net. This name comes from the fact that our model adds a cross-attention module at the bottom of the U-net structure to form a Y structure. We integrate the advantages of the two latest SOTA models, MedNeXt and SwinUNETR, and use Swin Transformer as the encoder and ConvNeXt as the decoder to innovatively design the Swin-NeXt structure. Our model uses the lowest-level feature map of the encoder as Key and Value and uses patient features such as pathology and treatment information as Query to calculate the attention weights in a Cross Attention module. Moreover, we simplify some pre- and post-processing as well as data enhancement methods in 3D image segmentation based on the dynUnet and nnU-net frameworks. We integrate our proposed Swin-NeXt with Cross-Attention framework into this framework. Last, we construct a DiceFocalCELoss to improve the training efficiency for the uneven data convergence of voxel classification.