  Efficiently capturing multi-scale information and building long-range
dependencies among pixels are essential for medical image segmentation because
of the various sizes and shapes of the lesion regions or organs. In this paper,
we present Multi-scale Cross-axis Attention (MCA) to solve the above
challenging issues based on the efficient axial attention. Instead of simply
connecting axial attention along the horizontal and vertical directions
sequentially, we propose to calculate dual cross attentions between two
parallel axial attentions to capture global information better. To process the
significant variations of lesion regions or organs in individual sizes and
shapes, we also use multiple convolutions of strip-shape kernels with different
kernel sizes in each axial attention path to improve the efficiency of the
proposed MCA in encoding spatial information. We build the proposed MCA upon
the MSCAN backbone, yielding our network, termed MCANet. Our MCANet with only
4M+ parameters performs even better than most previous works with heavy
backbones (e.g., Swin Transformer) on four challenging tasks, including skin
lesion segmentation, nuclei segmentation, abdominal multi-organ segmentation,
and polyp segmentation. Code is available at
https://github.com/haoshao-nku/medical_seg.
