Dense Convolutional Network and Its Application in Medical Image Analysis.

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of D...

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Published in:BioMed Research International pp. 1 - 23
Main Authors: Zhou, Tao, Ye, XinYu, Lu, HuiLing, Zheng, Xiaomin, Qiu, Shi, Liu, YunCan
Format: diagnostic images pictorial review tables/charts Journal Article
Published: Wiley-Blackwell 4/25/2022
Online Access:View this record in EBSCOhost
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      dt: 4/25/2022
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2022/2384830
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        atl: Dense Convolutional Network and Its Application in Medical Image Analysis.
      aug:
        au:
          Zhou, Tao
          Ye, XinYu
          Lu, HuiLing
          Zheng, Xiaomin
          Qiu, Shi
          Liu, YunCan
        affil: School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
      sug:
        subj:
          Neural Networks (Computer)
          Image Processing, Computer Assisted
          Image Interpretation, Computer Assisted
          Deep Learning Methods
          Research, Medical
      ab: Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        pictorial
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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