TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

02/16/2021 ∙ by Yundong Zhang, et al. ∙ 0

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.



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