  Medical image segmentation presents the challenge of segmenting various-size
targets, demanding the model to effectively capture both local and global
information. Despite recent efforts using CNNs and ViTs to predict annotations
of different scales, these approaches often struggle to effectively balance the
detection of targets across varying sizes. Simply utilizing local information
from CNNs and global relationships from ViTs without considering potential
significant divergence in latent feature distributions may result in
substantial information loss. To address this issue, in this paper, we will
introduce a novel Stagger Network (SNet) and argues that a well-designed fusion
structure can mitigate the divergence in latent feature distributions between
CNNs and ViTs, thereby reducing information loss. Specifically, to emphasize
both global dependencies and local focus, we design a Parallel Module to bridge
the semantic gap. Meanwhile, we propose the Stagger Module, trying to fuse the
selected features that are more semantically similar. An Information Recovery
Module is further adopted to recover complementary information back to the
network. As a key contribution, we theoretically analyze that the proposed
parallel and stagger strategies would lead to less information loss, thus
certifying the SNet's rationale. Experimental results clearly proved that the
proposed SNet excels comparisons with recent SOTAs in segmenting on the Synapse
dataset where targets are in various sizes. Besides, it also demonstrates
superiority on the ACDC and the MoNuSeg datasets where targets are with more
consistent dimensions.
