融合Swin与ConvNeXt的跨注意力3D医学图像分割模型
nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images Segmentation
- 在U-Net基础上加入跨注意力模块，用病理信息作为查询增强特征
- 在腹部CT数据集上达到86.7% Dice分数，优于现有SOTA方法
- 适合需要融合临床信息的医学影像分析任务
本文提出一种新型3D医学图像分割模型nnY-Net。该名称源于在U-Net结构底部引入跨注意力模块形成类似'Y'的结构。模型融合了当前最先进的MedNeXt与SwinUNETR的优势，采用Swin Transformer作为编码器、ConvNeXt作为解码器，创新性地构建Swin-NeXt架构。模型利用编码器的底层特征图作为键（Key）和值（Value），将患者病理及治疗等临床信息作为查询（Query），通过跨注意力模块计算注意力权重。此外，基于dynUnet和nnU-net框架，简化了部分预处理、后处理及数据增强流程。最后，设计DiceFocalCELoss以提升不均衡体素分类下的训练效率。
原文摘要 · Abstract (English)
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.
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