Y-CA-Net：一种用于体积医学图像分割的基于卷积注意力的网络
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
AI总结：
针对注意力体积分割缺乏判别性局部特征的问题，本文并行融合卷积编码器与transformer/混合注意力主干，提出Y-CT-Net、Y-CH-Net及通用Y-CA-Net架构，在多器官分割中提升Dice与HD95表现。
摘要：
近期基于注意力的体积分割（VS）方法在医学领域取得了显著性能，其重点在于对长程依赖进行建模。然而，对于体素级预测任务而言，具有判别力的局部特征是VS模型性能的关键组成部分，而这正是基于注意力的VS方法所缺失的。为解决这一问题，我们有意将卷积编码器分支与transformer主干相结合，以并行方式提取局部和全局特征，并在跨特征混合模块（Cross Feature Mixer Module，CFMM）中对其进行聚合，从而更好地预测分割掩码。因此，我们观察到所得模型Y-CT-Net在多项医学分割任务上取得了具有竞争力的性能。例如，在多器官分割中，Y-CT-Net取得了82.4%的Dice分数，比经过良好调优的VS Transformer/CNN类基线UNETR/ResNet-3D分别高出2.9%/1.4%。在Y-CT-Net取得成功后，我们利用混合注意力模型扩展了这一概念，由此得到Y-CH-Net模型；对于同一项分割任务，该模型在HD95分数方面带来了3%的提升。Y-CT-Net和Y-CH-Net两个模型的有效性验证了我们的假设，并促使我们提出Y-CA-Net这一概念：这是一种基于任意两个编码器和一个解码器主干的通用、灵活架构，旨在充分利用卷积机制与注意力机制的互补优势。基于实验结果，我们认为Y-CA-Net是在体积分割中取得优异结果的关键参与者。
英文摘要：
Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, for voxel-wise prediction tasks, discriminative local features are key components for the performance of the VS models which is missing in attention-based VS methods. Aiming at resolving this issue, we deliberately incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner and aggregate them in Cross Feature Mixer Module (CFMM) for better prediction of segmentation mask. Consequently, we observe that the derived model, Y-CT-Net, achieves competitive performance on multiple medical segmentation tasks. For example, on multi-organ segmentation, Y-CT-Net achieves an 82.4% dice score, surpassing well-tuned VS Transformer/CNN-like baselines UNETR/ResNet-3D by 2.9%/1.4%. With the success of Y-CT-Net, we extend this concept with hybrid attention models, that derived Y-CH-Net model, which brings a 3% improvement in terms of HD95 score for same segmentation task. The effectiveness of both models Y-CT-Net and Y-CH-Net verifies our hypothesis and motivates us to initiate the concept of Y-CA-Net, a versatile generic architecture based upon any two encoders and a decoder backbones, to fully exploit the complementary strengths of both convolution and attention mechanisms. Based on experimental results, we argue Y-CA-Net is a key player in achieving superior results for volumetric segmentation.
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。
