Semantic similarity with cross modal medical image online hashing for internet of medical things✩

Qinghai Liu; Lun Tang; Qianlin Wu; Jia Luo

doi:10.1016/j.dcan.2025.07.011

›› 2026, Vol. 12 ›› Issue (5) :845 -854. DOI: 10.1016/j.dcan.2025.07.011

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Semantic similarity with cross modal medical image online hashing for internet of medical things^✩

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^a School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications , Chongqing 400065, China

^b School of Cyber Security and Information law, Chongqing University of Posts and Telecommunications , Chongqing 400065, China

^* E-mail address: luojia@cqupt.edu.cn (J. Luo).

Qinghai Liu received the M.S. degree from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2015. He is currently pursuing the Ph.D. in communication and information systems from the Chongqing University of Posts and Telecommunications. His current research interests include wireless network communication, image processing and deep reinforcement learning.

Lun Tang received the Ph.D. degree in communication and information system from Chongqing University, Chongqing, China. He is currently a professor with the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications. His current research interests include 6G cellular networks, network slicing, and machine learning.

Qianlin Wu received the B.S. degree in Chengdu University of Information Science and Technology, Chengdu, China, in 2022. He is currently pursuing the M.S. degree with the Key Laboratory of Mobile Communication Technology Chongqing University of Posts and Telecommunications, Chongqing, China. His research interests include digital twin networks, and image processing.

Jia Luo received the Ph.D. degree from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2020. He is currently a lecturer with Chongqing University of Posts and Telecommunications. From April 2018 to April 2019, he is a visiting scholar with Carleton University, Ottawa, ON, Canada. His current research interests include UAV communications, mobile edge computing and artificial intelligence algorithms.

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Abstract

Online hashing methods are receiving increasing attention in cross modal medical image retrieval research. However, existing online methods often lack the learning ability to maintain semantic correlation between new and existing data. This paper proposes an Online Semantic-similarity Cross Modal Hashing (OSCMH) learning framework that can incrementally learn compact binary hash codes of streaming data. Considering the dynamic nature of data streams in online hashing methods, the proposed learning framework is implemented under the Internet of Medical Things (IoMT) architecture, where IoMT enables real-time data transmission and processing through edge computing resources, effectively supporting the storage and analysis of medical image data. Then, a sparse representation of existing data based on online anchor datasets is designed to avoid semantic forgetting of the data and adaptively update hash codes, effectively maintaining semantic correlation between existing and arriving data, reducing information loss and improving training efficiency. Finally, an online discrete optimization method is proposed to solve the binary optimization problem by incrementally updating the hash function and optimizing the hash code on the stream data points. A large number of experiments on the benchmark datasets have shown that the algorithm proposed in this paper can effectively improve the retrieval efficiency in the field of medical images compared to existing online or offline hashing methods.

Keywords

Online hashing / Cross modal retrieval / Medical image / Discrete optimization

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Qinghai Liu, Lun Tang, Qianlin Wu, Jia Luo. Semantic similarity with cross modal medical image online hashing for internet of medical things^✩. , 2026, 12 (5) : 845-854 DOI:10.1016/j.dcan.2025.07.011

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CRediT authorship contribution statement

Qinghai Liu: Writing -- review & editing, Writing -- original draft, Software. Lun Tang: Supervision, Funding acquisition. Qianlin Wu: Software, Formal analysis, Data curation. Jia Luo: Funding acquisition, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The National Natural Science Foundation of China (62071078), the Sichuan Science and Technology Program (2021YFQ0053), and Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202400643).

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