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首页> 外文期刊>Computer Communications >Class consistent and joint group sparse representation model for image classification in Internet of Medical Things
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Class consistent and joint group sparse representation model for image classification in Internet of Medical Things

机译：类别一致和联合组稀疏表示模型在医学互联网上的图像分类

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摘要

The amount of data handled by Internet of Medical Things (IoMT) devices grows exponentially, which means higher exposure of sensitive data. The security and privacy of the data collected from IoMT devices, either during their transmission to a cloud or while stored in a cloud, are major unresolved matters. Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis, but most of traditional HEp-2 cell classification algorithms dramatically rely on a single modal feature or fuse different modality features based on fixed weighted schemes, with the result that the complementary information of multimodal features will be not reasonably utilized. In this paper, a class consistent and joint group sparse representation model (CCJGSR) is proposed, expresses the test data through the sparse linear combination of training data and constrains the observations from different modalities of the test object to share their sparse statements. Group sparse representation can fully explore the complementary relationships among different modality features. At the same time, the objective function embeds both the group regularization terms and class consistent, where they enforce the intuitive constraint which the predicted class labels are consistent across all modalities. The experimental results on the HEp2 cell dataset indicate that our proposed algorithm is robust and efficient, and it outperforms existing approaches.
机译：通过互联网（IOMT）设备处理的数据量呈指数增长，这意味着敏感数据的曝光更高。从IOMT设备收集的数据的安全性和隐私，无论是在传输到云端还是存储在云中时都是主要的未解决问题。自动化人喉癌（HEP-2）细胞分类对于医学诊断至关重要，但大多数传统的HEP-2细胞分类算法大大依赖于基于固定加权方案的单个模态特征或熔断器不同的模态特征，结果是多式联特征的互补信息将不合理地利用。在本文中，提出了一个类一致和联合组稀疏表示模型（CCJGSR），通过训练数据的稀疏线性组合表示测试数据，并将观察从测试对象的不同模式中的分享分享到共享它们的稀疏语句。小组稀疏表示可以完全探索不同的模态特征之间的互补关系。同时，目标函数嵌入了组正则化术语和类一致，在那里他们强制执行预测类标签在所有模式上一致的直观约束。 HEP2电池数据集上的实验结果表明我们所提出的算法具有稳健和高效，并且优于现有方法。

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  • 来源
    《Computer Communications》 |2021年第1期|57-65|共9页
  • 作者

    Gao Zan; Yang Yuchan; Khosravi Mohammad R.; Wan Shaohua;

  • 作者单位

    Qilu Univ Technol Shandong Acad Sci Shandong Artificial Intelligence Inst Jinan 250014 Peoples R China;

    Qilu Univ Technol Shandong Acad Sci Shandong Artificial Intelligence Inst Jinan 250014 Peoples R China;

    Persian Gulf Univ Dept Comp Engn Bushehr Iran|Shiraz Univ Technol Dept Elect & Elect Engn Telecommun Grp Shiraz Iran;

    Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China|Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Internet of Medical Things; Joint group sparse; Class consistent; Reliability; Feature fusion;

    机译：医疗器互联网;联合集团稀疏;类一致;可靠性;特征融合;

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