  U-Net based convolutional neural networks with deep feature representation
and skip-connections have significantly boosted the performance of medical
image segmentation. In this paper, we study the more challenging problem of
improving efficiency in modeling global contexts without losing localization
ability for low-level details. TransFuse, a novel two-branch architecture is
proposed, which combines Transformers and CNNs in a parallel style. With
TransFuse, both global dependency and low-level spatial details can be
efficiently captured in a much shallower manner. Besides, a novel fusion
technique - BiFusion module is proposed to fuse the multi-level features from
each branch. TransFuse achieves the newest state-of-the-arts on polyp
segmentation task, with 20\% fewer parameters and the fastest inference speed
at about 98.7 FPS.
