  The recent vision transformer(i.e.for image classification) learns non-local
attentive interaction of different patch tokens. However, prior arts miss
learning the cross-scale dependencies of different pixels, the semantic
correspondence of different labels, and the consistency of the feature
representations and semantic embeddings, which are critical for biomedical
segmentation. In this paper, we tackle the above issues by proposing a unified
transformer network, termed Multi-Compound Transformer (MCTrans), which
incorporates rich feature learning and semantic structure mining into a unified
framework. Specifically, MCTrans embeds the multi-scale convolutional features
as a sequence of tokens and performs intra- and inter-scale self-attention,
rather than single-scale attention in previous works. In addition, a learnable
proxy embedding is also introduced to model semantic relationship and feature
enhancement by using self-attention and cross-attention, respectively. MCTrans
can be easily plugged into a UNet-like network and attains a significant
improvement over the state-of-the-art methods in biomedical image segmentation
in six standard benchmarks. For example, MCTrans outperforms UNet by 3.64%,
3.71%, 4.34%, 2.8%, 1.88%, 1.57% in Pannuke, CVC-Clinic, CVC-Colon, Etis,
Kavirs, ISIC2018 dataset, respectively. Code is available at
https://github.com/JiYuanFeng/MCTrans.
