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DETA-Net: A Dual Encoder Network with Text-Guided Attention Mechanism for Skin-Lesions Segmentation

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14088))

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Abstract

Skin-lesions segmentation plays a prominent role in computer-aided diagnosis systems for skin cancer, especially the remarkable success of the convolutional neural network (CNN) approaches in skin-lesions segmentation. However, it faces intractable challenges such as variable shape and blurred skin lesions boundaries. To this end, past research has employed cutting-edge mechanisms, including diverse attention modules. Inspired by state-of-the-art works, this study proposed a Dual Encoder framework with a Text-Guided Attention Network (DETA-Net) which can accurately and efficiently segment various and blurred lesions. Firstly, we designed a multi-scale joint encoder that took the advantage of both the CNNs and Transformer to extract features under the blurred lesion background condition. In addition, we introduced text-guided attention to propel classification in the manner of text-based embedding in the DETA-Net so that the variation in the size and number of the lesion can be efficiently accommodated. Experimental results demonstrated that DETA-Net provided better performance across multiple datasets compared with state-of-the-art on variable-sized skin lesion datasets in Skin-Cancer detection. We also evaluated the effectiveness of DETA-Net through extensive ablation studies on three different datasets, including ISIC 2016, ISIC 2018, and PH2 datasets. The baseline achieved 0.8838 Dice on ISIC 2016, 0.8864 Dice on ISIC 2018, and 0.8695 Dice on PH2.

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Acknowledgement

The authors gratefully acknowledge the reviewers for their valuable comments and suggestions. We would also like to thank our colleagues. This work is sponsored in part by National Natural Science Foundation of China (No. 62106175, 62072107), Natural Science Foundation of Fujian Province (2020J01610), and Postgraduate Scientific Research Innovation Practice Program of Tianjin University of Technology (YJ2249).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, People’s Republic of China

    Cong Shen & Xinyue Wang

  2. Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, People’s Republic of China

    Cong Shen & Xinyue Wang

  3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People’s Republic of China

    Jijun Tang

  4. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, U.S.

    Jijun Tang

  5. School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People’s Republic of China

    Zhijun Liao

Authors
  1. Cong Shen
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  2. Xinyue Wang
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  3. Jijun Tang
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  4. Zhijun Liao
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Corresponding author

Correspondence to Cong Shen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shen, C., Wang, X., Tang, J., Liao, Z. (2023). DETA-Net: A Dual Encoder Network with Text-Guided Attention Mechanism for Skin-Lesions Segmentation. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_3

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  • DOI: https://doi.org/10.1007/978-981-99-4749-2_3

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  • Online ISBN: 978-981-99-4749-2

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