  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.
