Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (1): 194-204.DOI: 10.3778/j.issn.1673-9418.2007038

• Artificial Intelligence • Previous Articles     Next Articles

Label Distribution Learning for Computer Aided Diagnosis of Multi-class ASD Classification

ZHANG Fengyexin1, WANG Jun2,+(), JIA Xiuyi3, PAN Xiang1, DENG Zhaohong1, SHI Jun2, WANG Shitong1   

  1. 1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
    3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2020-07-13 Revised:2020-11-03 Online:2022-01-01 Published:2020-11-25
  • About author:ZHANG Fengyexin, born in 1996, M.S. candidate. His research interests include artificial intelligence and pattern recognition.
    WANG Jun, born in 1978, associate professor. His research interests include pattern recognition and artificial intelligence.
    JIA Xiuyi, born in 1983, associate professor. His research interests include machine learning, granular computing and data mining.
    PAN Xiang, born in 1984, associate professor. His research interest is medical image processing.
    DENG Zhaohong, born in 1981, professor. His research interests include uncertainty artificial intelligence and its applications.
    SHI Jun, born in 1977, professor. His research interests include uncertainty medical image analysis, medical signal processing, etc.
    WANG Shitong, born in 1964, professor. His research interests include artificial intelligence, pattern recognition, etc.
  • Supported by:
    National Natural Science Foundation of China(61773208);Natural Science Foundation of Jiangsu Province(BK20181339);Natural Science Foundation of Jiangsu Province(BK20191287)

面向多分类自闭症辅助诊断的标记分布学习

章枫叶欣1, 王骏2,+(), 贾修一3, 潘祥1, 邓赵红1, 施俊2, 王士同1   

  1. 1.江南大学 人工智能与计算机学院,江苏 无锡 214122
    2.上海大学 通信与信息工程学院,上海 200444
    3.南京理工大学 计算机科学与工程学院,南京 210094
  • 通讯作者: + E-mail: wangjun_shu@shu.edu.cn
  • 作者简介:章枫叶欣(1996—),男,浙江丽水人,硕士研究生,主要研究方向为人工智能、模式识别。
    王骏(1978—),男,江苏苏州人,副教授,主要研究方向为模式识别、人工智能。
    贾修一(1983—),男,副教授,主要研究方向为机器学习、粒计算、数据挖掘。
    潘祥(1984—),男,副教授,主要研究方向为医学图像处理。
    邓赵红(1981—),男,安徽蒙城人,教授,主要研究方向为不确定性人工智能及其应用。
    施俊(1977—),男,教授,主要研究方向为不确定性医学图像分析、医学信号处理等。
    王士同(1964—),男,江苏扬州人,教授,主要研究方向为人工智能、模式识别等。
  • 基金资助:
    国家自然科学基金(61773208);江苏省自然科学基金(BK20181339);江苏省自然科学基金(BK20191287)

Abstract:

Autism spectrum disorder (ASD) is a series of complex neurodevelopmental disorders, including several diseases related to developmental disorders, but most of the existing diagnosis methods for autism are binary classification methods which cannot meet the actual needs. In addition, the label noise contained in ASD data, as well as the characteristics of high dimensionality and data imbalance, has brought great challenges to traditional methods. To this end, a new computer aided diagnosis method of ASD is proposed. This method solves the label noise by introducing label distribution learning (LDL), introduces a cost-sensitive mechanism to solve the data imbalance, uses label distribution support vector regression (SVR) to solve the classification difficulties caused by high-dimensional features by mapping samples to the feature space, and finally realizes the computer aided diagnosis of multi-class ASD. Experimental results show that compared with the existing methods, the proposed method overcomes the imbalance of the influence of the majority class and the minority class on the results, can effectively solve the class imbalance in ASD diagnosis, and has better and stable classification performance, which can assist in the diagnosis of ASD.

Key words: computer aided diagnosis of autism spectrum disorder (ASD), cost-sensitive mechanism, label distribution learning (LDL), support vector regression (SVR)

摘要:

自闭症谱性障碍(ASD)是一系列复杂的神经发展障碍性疾病,其包括若干与发育障碍相关的疾病,但是现有的自闭症辅助诊断方法大多是二分类方法,无法满足现实的需要。此外,ASD数据包含的标记噪声,以及高维度、数据分布不平衡等特点给传统分类方法带来了巨大的挑战。为此,提出一种新型的ASD辅助诊断方法,该方法通过引入标记分布学习(LDL)来解决标记噪声问题,引入代价敏感机制来解决样本不平衡问题,并采用基于支持向量回归(SVR)的标记分布学习方法,通过将样本映射到特征空间,解决高维特征带来的分类困难,最终实现多分类ASD的辅助诊断。实验结果表明,与已有方法比较,所提方法克服了多数类和少数类对结果的影响的不平衡性,可以有效地解决ASD诊断中的不平衡数据问题,拥有更好且稳定的分类性能,可以辅助ASD的诊断。

关键词: 自闭症谱性障碍(ASD)辅助诊断, 代价敏感机制, 标记分布学习(LDL), 支持向量回归(SVR)

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