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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation
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A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation

机译：具有共享跨域转移潜在空间的新型负转移抗性模糊聚类模型及其在脑CT图像分割中的应用

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摘要

Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms.
机译：用于医学图像分割的传统聚类算法只能在相对理想的条件下实现令人满意的聚类性能，其中来自相同分布的足够数据，并且数据很少被噪声或异常值扰乱。然而，具有代表性手动标签的足够量的医学图像通常不可用，因为经常用不同的扫描仪（或不同的扫描协议）或被各种噪声污染的医学图像。转移学习通过利用相关领域的知识来改善目标领域的学习。给定一些目标数据，转移学习的性能由源域和目标域之间的相关程度决定。为了实现阳性转移并避免负转移，通过计算转移知识的重量来提出负转移抗性机制。通过将负转移和最大平均差异（MMD）集成到模糊C-框架中，提出利用共享跨域转移潜在空间（称为NTR-FC-SCT）提取负传输抗性模糊聚类模型（称为NTR-FC-SCT）。意味着聚类。实验结果表明，建议的NTR-FC-SCT模型优于几种传统的非转移和相关转移聚类算法。

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  • 作者

    Jiang Yizhang; Gu Xiaoqing; Wu Dongrui; Hang Wenlong; Xue Jing; Qiu Shi; Lin Chin-Teng;

  • 作者单位

    Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China|Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China;

    Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China;

    Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China;

    Nanjing Tech Univ Sch Comp Sci & Technol Nanjing 211816 Jiangsu Peoples R China|Nanjing Med Univ Sch Comp Sci & Technol Wuxi 214023 Jiangsu Peoples R China;

    Nanjing Med Univ Dept Nephrol Affiliated Wuxi Peoples Hosp Wuxi 214023 Jiangsu Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Shaanxi Peoples R China;

    Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo NSW 2007 Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Medical image segmentation; fuzzy clustering; transfer learning; negative transfer;

    机译：医学图像分割;模糊聚类;转移学习;负转移;

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