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

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.