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Supervised deep semantics-preserving hashing for real-time pulmonary nodule image retrieval

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Abstract

Hashing-based medical image retrieval has drawn extensive attention recently, which aims at providing effective aided diagnosis for medical personnel. In the paper, a novel deep hashing framework is proposed in the medical image retrieval, where the processes of deep feature extraction, binary code learning, and deep hash function learning are jointly carried out in supervised fashion. Particularly, the discrete constrained objective function in the hash code learning is optimized iteratively, where the binary code can be directly solved with no need for relaxation. In the meantime, the semantic similarity is maintained by fully exploring supervision information during the discrete optimization, where the neighborhood structure of training data is preserved by applying a graph regularization term. Additionally, to gain the fine-grained ranking of the returned medical images sharing the same Hamming distance, a novel image re-ranking scheme is proposed to refine the similarity measurement by jointly considering Euclidean distance between the real-valued feature descriptors and their category information between those images. Extensive experiments on the pulmonary nodule image dataset demonstrate that the proposed method can achieve better retrieval performance over the state of the arts.

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Notes

  1. It is renamed as ’latent layer’ in this paper for more intuitive understanding.

  2. In the training set, pulmonary nodules are classified into six categories according to their malignant degree, see Sect. 4.

  3. https://www.tensorflow.org.

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Acknowledgements

This work is supported in part by the Natural Science Foundation of China under Grant 61702157 and 41804118, in part by Hebei Province Department of Education Fund under Grant QN2018085, in part by Innovation Capacity Improvement Project of Hebei Province 199676146H, and in part by the Key Program from NSF of North China Institute of Aerospace Engineering under Grant ZD-2013-05.

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China

    Yongjun Qi & Junhua Gu

  2. Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China

    Yongjun Qi & Junhua Gu

  3. Information Technology Center, North China Institute of Aerospace Engineering, Langfang, China

    Yongjun Qi

  4. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

    Junhua Gu, Yajuan Zhang & Feng Wang

  5. School of Computing and Communications, Lancaster University, Lancaster, UK

    Gengshen Wu

  6. Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China

    Junhua Gu & Yajuan Zhang

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  1. Yongjun Qi
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Correspondence to Junhua Gu.

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Qi, Y., Gu, J., Zhang, Y. et al. Supervised deep semantics-preserving hashing for real-time pulmonary nodule image retrieval. J Real-Time Image Proc 17, 1857–1868 (2020). https://doi.org/10.1007/s11554-020-00963-2

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