Laser & Optoelectronics Progress, Volume. 58, Issue 12, 1210006(2021)

Skin Lesion Segmentation Based on U-Shaped Structure Context Encoding and Decoding Network

Xinhui Jiang1 and Zhe Li2、*
Author Affiliations
  • 1School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China
  • 2Network and Information Technology Center, Xinjiang University, Urumqi, Xinjiang 830046, China
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    References(23)

    [3] Humayun J, Malik A S, Kamel N et al. Multilevel thresholding for segmentation of pigmented skin lesions[C]. //2011 IEEE International Conference on Imaging Systems and Techniques, May 17-18, 2011, Batu Ferringhi, Malaysia., 310-314(2011).

    [4] Chakkaravarthy A P, Chandrasekar A. Anautomatic segmentation of skin lesion from dermoscopy images using watershed segmentation[C]. //2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC), March 20-22, 2018, Malaysia, Malaysia., 15-18(2018).

    [10] Shahin A H, Amer K, Elattar M A et al. Deep convolutional encoder-decoders with aggregated multi-resolution skip connections for skin lesion segmentation[C]. //2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), April 8-11, 2019, Venice, Italy., 451-454(2019).

    [11] Mishra R, Daescu O. Deep learning for skin lesion segmentation[C]. //2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 13-16, 2017, Kansas City, MO, USA., 1189-1194(2017).

    [12] Liu L N, Mou L C, Zhu X X et al. Skin lesion segmentation based on improved U-Net[C]. //2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), May 5-8, 2019, Edmonton, AB, Canada., 1-4(2019).

    [13] Bi L, Feng D G, Fulham M et al. Improving skin lesion segmentation via stacked adversarial learning[C]. //2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), April 8-11, 2019, Venice, Italy., 1100-1103(2019).

    [14] Ronneberger O, Fischer P, Brox T et al. U-Net: convolutional networks for biomedical image segmentation[M]. // Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

    [17] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

    [18] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5987-5995(2017).

    [19] Wang Q L, Wu B G, Zhu P F et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, 11531-11539(2020).

    [20] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

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    Xinhui Jiang, Zhe Li. Skin Lesion Segmentation Based on U-Shaped Structure Context Encoding and Decoding Network[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210006

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    Paper Information

    Category: Image Processing

    Received: Aug. 13, 2020

    Accepted: Oct. 14, 2020

    Published Online: Jun. 18, 2021

    The Author Email: Li Zhe (13899919604@163.com)

    DOI:10.3788/LOP202158.1210006

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