In recent years, medical image segmentation has become an important
application in the field of computer-aided diagnosis. In this paper, we are the
first to propose a new graph convolution-based decoder namely, Cascaded Graph
Convolutional Attention Decoder (G-CASCADE), for 2D medical image segmentation.
G-CASCADE progressively refines multi-stage feature maps generated by
hierarchical transformer encoders with an efficient graph convolution block.
The encoder utilizes the self-attention mechanism to capture long-range
dependencies, while the decoder refines the feature maps preserving long-range
information due to the global receptive fields of the graph convolution block.
Rigorous evaluations of our decoder with multiple transformer encoders on five
medical image segmentation tasks (i.e., Abdomen organs, Cardiac organs, Polyp
lesions, Skin lesions, and Retinal vessels) show that our model outperforms
other state-of-the-art (SOTA) methods. We also demonstrate that our decoder
achieves better DICE scores than the SOTA CASCADE decoder with 80.8% fewer
parameters and 82.3% fewer FLOPs. Our decoder can easily be used with other
hierarchical encoders for general-purpose semantic and medical image
segmentation tasks.

近年来，医学图像分割已成为计算机辅助诊断领域的重要应用。本文首次提出了一种新的基于图卷积的解码器，即级联图卷积注意力解码器（G-CASCADE），用于 2D 医学图像分割。通过有效的图卷积块，G-CASCADE 逐渐改进由分层 Transformer 编码器生成的多阶段特征图。编码器利用自注意机制来捕捉长程依赖性，而解码器通过图卷积块的全局感受野来改进特征图以保留长程信息。在对五个医学图像分割任务（即腹部器官、心脏器官、息肉病变、皮肤病变和视网膜血管）进行了严格评估后，我们的解码器在性能方面优于其他最先进的方法。此外，我们还证明了我们的解码器在参数数量减少 80.8% 和浮点运算量减少 82.3% 的情况下实现了更好的 DICE 分数。我们的解码器可以与其他分层编码器轻松结合，用于通用的语义和医学图像分割任务。