  Medical image segmentation - the prerequisite of numerous clinical needs -
has been significantly prospered by recent advances in convolutional neural
networks (CNNs). However, it exhibits general limitations on modeling explicit
long-range relation, and existing cures, resorting to building deep encoders
along with aggressive downsampling operations, leads to redundant deepened
networks and loss of localized details. Hence, the segmentation task awaits a
better solution to improve the efficiency of modeling global contexts while
maintaining a strong grasp of low-level details. In this paper, we propose a
novel parallel-in-branch architecture, TransFuse, to address this challenge.
TransFuse combines Transformers and CNNs in a parallel style, where 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
created to efficiently fuse the multi-level features from both branches.
Extensive experiments demonstrate that TransFuse achieves the newest
state-of-the-art results on both 2D and 3D medical image sets including polyp,
skin lesion, hip, and prostate segmentation, with significant parameter
decrease and inference speed improvement.
