期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 150, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.106197
关键词
Medical image segmentation; Residual attention; Channel attention; Cross fusion; Cell segmentation; Fundus vessel segmentation
类别
资金
- National Natural Science Foundation of China [61772319, 62002200, 62202268, 62176140]
- Shandong Provincial Science and Technology Support Program of Youth Innovation Team in Colleges [2021KJ069, 2019KJN042]
- Yantai science and technology innovation development plan [2022JCYJ031]
In this paper, we propose a feature attention network based on dual encoder, which achieves excellent performance in learning and extracting detailed features. It has important applications in the field of medical image segmentation.
In recent years, variant networks derived from U-Net networks have achieved better results in the field of medical image segmentation. However, we found during our experiments that the current mainstream networks still have certain shortcomings in the learning and extraction of detailed features. Therefore, in this paper, we propose a feature attention network based on dual encoder. In the encoder stage, a dual encoder is used to implement macro feature extraction and micro feature extraction respectively. Feature attention fusion is then performed, resulting in the network that not only performs well in the recognition of macro features, but also in the processing of micro features, which is significantly improved. The network is divided into three stages: (1) learning and capture of macro features and detail features with dual encoders; (2) completing the mutual complementation of macro features and detail features through the residual attention module; (3) complete the fusion of the two features and output the final prediction result. We conducted experiments on two datasets on DEAU-Net and from the results of the comparison experiments, we showed better results in terms of edge detail features and macro features processing.
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