  Recently, a variety of vision transformers have been developed as their
capability of modeling long-range dependency. In current transformer-based
backbones for medical image segmentation, convolutional layers were replaced
with pure transformers, or transformers were added to the deepest encoder to
learn global context. However, there are mainly two challenges in a scale-wise
perspective: (1) intra-scale problem: the existing methods lacked in extracting
local-global cues in each scale, which may impact the signal propagation of
small objects; (2) inter-scale problem: the existing methods failed to explore
distinctive information from multiple scales, which may hinder the
representation learning from objects with widely variable size, shape and
location. To address these limitations, we propose a novel backbone, namely
ScaleFormer, with two appealing designs: (1) A scale-wise intra-scale
transformer is designed to couple the CNN-based local features with the
transformer-based global cues in each scale, where the row-wise and column-wise
global dependencies can be extracted by a lightweight Dual-Axis MSA. (2) A
simple and effective spatial-aware inter-scale transformer is designed to
interact among consensual regions in multiple scales, which can highlight the
cross-scale dependency and resolve the complex scale variations. Experimental
results on different benchmarks demonstrate that our Scale-Former outperforms
the current state-of-the-art methods. The code is publicly available at:
https://github.com/ZJUGiveLab/ScaleFormer.
