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DCFNet:An Effective Dual-Branch cross-Attention fusion Network for Medical Image Segmentation

Computer Modeling in Engineering & Sciences 2024年第7期140卷 1103-1128页

作者： Chengzhang Zhu Renmao Zhang Yalong Xiao Beiji Zou Xian Chai Zhangzheng Yang Rong Hu Xuanchu Duan School of Computer Science and Engineering Central South UniversityChangsha410083China School of Humanities Central South UniversityChangsha410083China Xiangya Hospital Central South University Changsha410008China Changsha Aier Eye Hospital Changsha410015China

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and ***,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant ***,there are some limitations in the current integration of CNN and Transformer technology in two key ***,most methods either overlook or fail to fully incorporate the complementary nature between local and global ***,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and *** address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local *** then designed the feature cross-fusion(FCF)module to efficiently fuse local and global *** the FCF,the utilization of the Channel-wise cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the feature fusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial ***,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the *** results demonstrate that DCFNet exhibits enhanced accuracy in segmentation *** to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of ***’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

关键词： Convolutional neural networks Swin Transformer dual branch medical image segmentation feature cross fusion

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