Great progress has been made in automatic medical image segmentation due to
powerful deep representation learning. The influence of transformer has led to
research into its variants, and large-scale replacement of traditional CNN
modules. However, such trend often overlooks the intrinsic feature extraction
capabilities of the transformer and potential refinements to both the model and
the transformer module through minor adjustments. This study proposes a novel
deep medical image segmentation framework, called DA-TransUNet, aiming to
introduce the Transformer and dual attention block into the encoder and decoder
of the traditional U-shaped architecture. Unlike prior transformer-based
solutions, our DA-TransUNet utilizes attention mechanism of transformer and
multifaceted feature extraction of DA-Block, which can efficiently combine
global, local, and multi-scale features to enhance medical image segmentation.
Meanwhile, experimental results show that a dual attention block is added
before the Transformer layer to facilitate feature extraction in the U-net
structure. Furthermore, incorporating dual attention blocks in skip connections
can enhance feature transfer to the decoder, thereby improving image
segmentation performance. Experimental results across various benchmark of
medical image segmentation reveal that DA-TransUNet significantly outperforms
the state-of-the-art methods. The codes and parameters of our model will be
publicly available at this https URL

本研究提出了一种名为 DA-TransUNet 的新型深度医学图像分割框架，将 Transformer 和双注意力块引入传统 U 形架构的编码器和解码器中，通过注意力机制和多方面特征提取来增强医学图像分割，并通过实验结果证明 DA-TransUNet 在各个医学图像分割基准测试中显著优于现有方法。