  Dense prediction in medical volume provides enriched guidance for clinical
analysis. CNN backbones have met bottleneck due to lack of long-range
dependencies and global context modeling power. Recent works proposed to
combine vision transformer with CNN, due to its strong global capture ability
and learning capability. However, most works are limited to simply applying
pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy
computation and little consideration for 3D data). Therefore, designing an
elegant and efficient vision transformer learner for dense prediction in
medical volume is promising and challenging. In this paper, we propose a novel
3D Shuffle-Mixer network of a new Local Vision Transformer-MLP paradigm for
medical dense prediction. In our network, a local vision transformer block is
utilized to shuffle and learn spatial context from full-view slices of
rearranged volume, a residual axial-MLP is designed to mix and capture
remaining volume context in a slice-aware manner, and a MLP view aggregator is
employed to project the learned full-view rich context to the volume feature in
a view-aware manner. Moreover, an Adaptive Scaled Enhanced Shortcut is proposed
for local vision transformer to enhance feature along spatial and channel
dimensions adaptively, and a CrossMerge is proposed to skip-connects the
multi-scale feature appropriately in the pyramid architecture. Extensive
experiments demonstrate the proposed model outperforms other state-of-the-art
medical dense prediction methods.
