Due to the scarcity and specific imaging characteristics in medical images,
light-weighting Vision Transformers (ViTs) for efficient medical image
segmentation is a significant challenge, and current studies have not yet paid
attention to this issue. This work revisits the relationship between CNNs and
Transformers in lightweight universal networks for medical image segmentation,
aiming to integrate the advantages of both worlds at the infrastructure design
level. In order to leverage the inductive bias inherent in CNNs, we abstract a
Transformer-like lightweight CNNs block (ConvUtr) as the patch embeddings of
ViTs, feeding Transformer with denoised, non-redundant and highly condensed
semantic information. Moreover, an adaptive Local-Global-Local (LGL) block is
introduced to facilitate efficient local-to-global information flow exchange,
maximizing Transformer's global context information extraction capabilities.
Finally, we build an efficient medical image segmentation model (MobileUtr)
based on CNN and Transformer. Extensive experiments on five public medical
image datasets with three different modalities demonstrate the superiority of
MobileUtr over the state-of-the-art methods, while boasting lighter weights and
lower computational cost. Code is available at
this https URL

轻量级视觉转换 (ViT) 在高效医学图像分割中的应用挑战，本研究重新关注了 CNN 和 Transformer 在轻量级医学图像分割中的关系，并提出了一种融合两者优势的基础架构设计，实现了有效的医学图像分割模型 (MobileUtr)。在五个公开医学图像数据集上的广泛实验验证了 MobileUtr 的优越性，同时具备更轻的权重和更低的计算成本。