基于在线语义相似跨模态医学图像哈希检索  

Cross modal medical image online hash retrieval based on online semantic similarity

作　　者：刘青海 唐伦[1] 吴钱林 徐黎明[2,3] 陈前斌[1] LIU Qinghai;TANG Lun;WU Qianlin;XU Liming;CHEN Qianbin(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China;School of Computer Science,China West Normal University,Nanchong,Sichuan 637009,P.R.China;College of Computer Science,Sichuan University,Chengdu 610065,P.R.China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]西华师范大学计算机学院,四川南充637000 [3]四川大学计算机学院,成都610065

出　　处：《生物医学工程学杂志》2025年第2期343-350,共8页Journal of Biomedical Engineering

基　　金：国家自然科学基金(62071078)。

摘　　要：在线哈希方法在跨模态医学图像检索中的研究越来越受到关注,然而现有的在线哈希方法缺乏保持现有数据与新增数据之间语义相关性的学习能力。对此,本文提出一种在线语义相似跨模态哈希检索学习框架,以增量学习方式学习医学流数据的紧凑二进制哈希码,并通过设计基于在线锚点数据集方法稀疏表达已有数据,避免遗忘已学习到的语义而且可以自适应更新哈希码,减少信息损失的同时提高训练效率,有效地保持现有数据和新增数据之间的语义相关性。此外,提出在线离散优化方法来解决哈希码离散优化问题,增量更新哈希函数并优化医学流数据的哈希码。与现有的在线或离线哈希方法相比,本文提出的算法在两个数据集上的平均检索精度分别提高了12.5%和14.3%,有效提升了医学图像领域的检索效率。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. To this end, we proposed online semantic similarity cross-modal hashing(OSCMH) learning framework to incrementally learn compact binary hash codes of medical stream data. Within it, a sparse representation of existing data based on online anchor datasets was designed to avoid semantic forgetting of the data and adaptively update hash codes, which effectively maintained semantic correlation between existing and arriving data and reduced information loss as well as improved training efficiency. Besides, an online discrete optimization method was proposed to solve the binary optimization problem of hash code by incrementally updating hash function and optimizing hash code on medical stream data. Compared with existing online or offline hashing methods, the proposed algorithm achieved average retrieval accuracy improvements of 12.5% and 14.3% on two datasets, respectively, effectively enhancing the retrieval efficiency in the field of medical images.

关 键 词：在线哈希 跨模态检索 医学图像 离散优化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]