Abstract
Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant architectures in medical image segmentation. Although CNNs can efficiently capture local representations, they experience difficulty establishing long-distance dependencies. Comparably, ViTs achieve impressive success owing to their powerful global contexts modeling capabilities, but they may not generalize well on insufficient datasets due to the lack of inductive biases inherent to CNNs. To inherit the merits of these two different design paradigms while avoiding their respective limitations, we propose a concurrent structure termed ConTrans, which can couple detailed localization information with global contexts to the maximum extent. ConTrans consists of two parallel encoders, i.e., a Swin Transformer encoder and a CNN encoder. Specifically, the CNN encoder is progressively stacked by the novel Depthwise Attention Block (DAB), with the aim to provide the precise local features we need. Furthermore, a well-designed Spatial-Reduction-Cross-Attention (SRCA) module is embedded in the decoder to form a comprehensive fusion of these two distinct feature representations and eliminate the semantic divergence between them. This allows to obtain accurate semantic information and ensure the up-sampling features with semantic consistency in a hierarchical manner. Extensive experiments across four typical tasks show that ConTrans significantly outperforms state-of-the-art methods on ten famous benchmarks.
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Acknowledgments
This work was supported in part by the NSFC fund (62176077, 61906162), in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019Bl515120055, in part by the Shenzhen Key Technical Project under Grant 2020N046, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20210324132210025, in part by the Medical Biometrics Perception and Analysis Engineering Laboratory, Shenzhen, China, in part by Shenzhen Science and Technology Program (RCBS20200714114910193), and in part by Education Center of Experiments and Innovations at Harbin Institute of Technology, Shenzhen.
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Lin, A., Xu, J., Li, J., Lu, G. (2022). ConTrans: Improving Transformer with Convolutional Attention for Medical Image Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_29
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