提出混合残差注意力机制，提升3D医学图像分割精度。

Vision Transformer shows great superiority in medical image segmentation due to the ability in learning long-range dependency. For medical image segmentation from 3D data, such as computed tomography (CT), existing methods can be broadly classified into 2D-based and 3D-based methods. One key limitation in 2D-based methods is that the intra-slice information is ignored, while the limitation in 3D-based methods is the high computation cost and memory consumption, resulting in a limited feature representation for inner-slice information. During the clinical examination, radiologists primarily use the axial plane and then routinely review both axial and coronal planes to form a 3D understanding of anatomy. Motivated by this fact, our key insight is to design a hybrid model which can first learn fine-grained inner-slice information and then generate a 3D understanding of anatomy by incorporating 3D information. We present a novel \textbf{H}ybrid \textbf{Res}idual trans\textbf{Former} \textbf{(HResFormer)} for 3D medical image segmentation. Building upon standard 2D and 3D Transformer backbones, HResFormer involves two novel key designs: \textbf{(1)} a \textbf{H}ybrid \textbf{L}ocal-\textbf{G}lobal fusion \textbf{M}odule \textbf{(HLGM)} to effectively and adaptively fuse inner-slice information from 2D Transformer and intra-slice information from 3D volumes for 3D Transformer with local fine-grained and global long-range representation. \textbf{(2)} a residual learning of the hybrid model, which can effectively leverage the inner-slice and intra-slice information for better 3D understanding of anatomy. Experiments show that our HResFormer outperforms prior art on widely-used medical image segmentation benchmarks. This paper sheds light on an important but neglected way to design Transformers for 3D medical image segmentation.