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Multi-task Class Feature Space Fusion Domain Adaptation Network for Thyroid Ultrasound Images: Research on Generalization of Smart Healthcare Systems

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Wireless Algorithms, Systems, and Applications (WASA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13471))

Abstract

In recent years, the poor generalizability of deep neural networks in multi-model medical images has attracted widespread attention. Domain adaptation is an approach to alleviate the above problem, which transfers the labeled source domain to the target domain. It can reduce the data labeling workload in the target domain and significantly improve the network’s generalizability. However, the differences between foreground areas and background areas of medical images are relatively minor, and it is difficult for existing methods to effectively extract domain invariant features. Further optimization of the feature distribution alignment for each category is also lacking. Therefore, a Multi-task Class feature space Fusion Domain Adaptation Network (MCFDAN) is proposed in this paper. Firstly, a reconstruction branch is added to the baseline network to mitigate feature offset of the target domain during encoding. Secondly, category constraints are added to the fusion of domain feature spaces, improving the generalizability of the source classifier to the target domain. Finally, the network incorporates a recurrent cross-attention module that highlights the feature expression of the lesion region. The evaluation results demonstrate that the proposed network achieves a significant performance improvement, which is important for the application of smart healthcare systems.

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Acknowledgments

This work was supported by Major Scientific and Technological Projects for A New Generation of Artificial Intelligence of Tianjin (Grant No. 18ZXZNSY00300).

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Xiang Ying, Zhen Liu, Jie Gao & Ruixuan Zhang

  2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Xiang Ying, Zhen Liu, Jie Gao & Ruixuan Zhang

  3. Tianjin Key Laboratory of Advanced Networking, Tianjin, China

    Xiang Ying, Zhen Liu, Jie Gao & Ruixuan Zhang

  4. OpenBayes (Tianjin) IT Co., Ltd., Tianjin, China

    Han Jiang

  5. Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

    Xi Wei

Authors
  1. Xiang Ying
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  2. Zhen Liu
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  4. Ruixuan Zhang
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Correspondence to Xi Wei .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Lei Wang

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Michael Segal

  3. Chang Gung University, Taiwan, China

    Jenhui Chen

  4. Tianjin University, Tianjin, China

    Tie Qiu

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Ying, X., Liu, Z., Gao, J., Zhang, R., Jiang, H., Wei, X. (2022). Multi-task Class Feature Space Fusion Domain Adaptation Network for Thyroid Ultrasound Images: Research on Generalization of Smart Healthcare Systems. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-19208-1_12

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  • Online ISBN: 978-3-031-19208-1

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