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Deep Discriminative Hashing for Cross-Modal Hashing Based Computer-Aided Diagnosis

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14358))

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Abstract

Massive medical data in multi-modalities emerges with the development of modern medicine, which facilitates the construction of computer-aided diagnosis (CAD) methods. However, most existing CAD methods diagnose diseases only based on the relevant single-modal data, and thus their applications are limited in single modality. To reveal intrinsic connections between heterogeneous modalities and further build multi-modal CAD methods, a novel cross-modal hashing model named Deep Discriminative Hashing (DDH) is proposed. Specifically, semantic labels are encoded to obtain a fixed classifier for the preservation of semantic similarity. Furthermore, benefiting from the classifier, the optimization of hash functions for different modalities is regarded as a classification task that aims to further consider the improvement of discriminability with angular softmax loss. Therefore, DDH projects medical multi-modal data into the common hamming space, and performs multi-modal CAD via cross-modal retrieval. Moreover, since the encoding procedure of different modalities is decoupled, DDH can also execute single-modal CAD based on the medical image retrieval. Experimental results demonstrate the superior accuracy of DDH compared with state-of-the-arts in both medical image retrieval and cross-modal medical data retrieval tasks.

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Author information

Authors and Affiliations

  1. School of Software, Hefei University of Technology, Hefei, 230009, China

    ChongShen Yang

  2. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China

    YuFeng Shi

Authors
  1. ChongShen Yang
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  2. YuFeng Shi
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Corresponding author

Correspondence to YuFeng Shi .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Yang, C., Shi, Y. (2023). Deep Discriminative Hashing for Cross-Modal Hashing Based Computer-Aided Diagnosis. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-46314-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46313-6

  • Online ISBN: 978-3-031-46314-3

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