...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Artificial intelligence in medicine >Handling imbalanced medical image data: A deep-learning-based one-class classification approach
【24h】

Handling imbalanced medical image data: A deep-learning-based one-class classification approach

机译：处理不平衡的医学图像数据：基于深度学习的单级分类方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods.
机译：在临床环境中，许多医学图像数据集遭受不平衡问题，妨碍了异常值的检测（罕见的医疗保健事件），因为大多数分类方法假设等同的类。通过这种方式，识别不平衡数据集中的异常值已成为一个至关重要的问题。为了帮助解决这一挑战，一个级别的分类，专注于使用来自单个给定类的样本学习模型，引起了越来越越来越关注。以前的单级建模通常使用功能映射或功能拟合来强制执行特征学习过程。然而，这些方法限于通常具有复杂特征的医学图像。在本文中，提出了一种新的方法，使得能够通过利用成像复杂性的概念来最佳地学习单级相关的固有成像特征。我们调查并比较简单但有效的扰动操作对图像的影响，以捕获成像复杂性并增强特征学习。在四个临床数据集上进行广泛的实验，以表明所提出的方法优于四种最先进的方法。

著录项

  • 来源
    《Artificial intelligence in medicine》 |2020年第8期|101935.1-101935.8|共8页
  • 作者

    Gao Long; Zhang Lei; Liu Chang; Wu Shandong;

  • 作者单位

    Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China|Univ Pittsburgh Sch Med Dept Radiol 4200 Fifth Ave Pittsburgh PA 15260 USA;

    Univ Pittsburgh Sch Med Dept Radiol 4200 Fifth Ave Pittsburgh PA 15260 USA;

    Univ Pittsburgh Dept Bioengn Swanson Sch Engn 4200 Fifth Ave Pittsburgh PA 15260 USA;

    Univ Pittsburgh Sch Med Dept Radiol 4200 Fifth Ave Pittsburgh PA 15260 USA|Univ Pittsburgh Dept Bioengn Swanson Sch Engn 4200 Fifth Ave Pittsburgh PA 15260 USA|Univ Pittsburgh Dept Biomed Informat 4200 Fifth Ave Pittsburgh PA 15260 USA|Univ Pittsburgh Intelligent Syst Program 4200 Fifth Ave Pittsburgh PA 15260 USA;

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

    Medical image classification; Data imbalance; Deep learning; Image complexity;

    机译：医学图像分类;数据不平衡;深入学习;图像复杂性;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号