Decision-augmented generative adversarial network for skin lesion segmentation

Feng Jiang, Feng Zhou, Jing Qin, Tianfu Wang, Baiying Lei

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)


Skin lesion is regarded as an illness, which is often detected and diagnosed by utilizing dermoscopy imaging. The essential step of skin lesion analysis is to segment the lesion region accurately. However, many variations in view and scale of lesion areas make segmentation quite challenging. In this paper, we propose an effective skin lesion segmentation architecture based on a modified generative adversarial network. As the network can learn great feature representation of dermoscopic images automatically in an unsupervised way, supervised learning could make it more promising. In particular, we explore an encoder-decoder network concatenating residual blocks and atrous convolution as the generator to generate the segmentation masks. This generator can obtain deep representation and preserve fine-grained information. The decision of the discriminator is very important to optimize a network. To enhance the reliability of the discrimination, two discriminators are applied to distinguish generated masks from ground truth jointly. The model is trained and evaluated using ISIC skin lesion challenge datasets in 2017. Our experiments demonstrate that the proposed network achieves the favorable segmentation performance compared with the other CNN-based supervised methods.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636411
Publication statusPublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • Atrous convolution
  • Augmented decision
  • Generative adversarial network
  • Skin lesion segmentation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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