...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Medical Imaging >Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative
【24h】

Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative

机译：用于早期检测膝关节骨关节炎的判别正则化自动编码器：来自骨关节炎倡议的数据

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

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized Auto Encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard Auto-Encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.
机译：骨关节炎（OA）是肌肉骨骼系统中最常见的疾病以及老年人之间减少移动性的主要原因。 OA的视觉评估仍然受到主观性的影响。最近，基于学习方法的计算机辅助诊断（CAD）系统显示出改善膝关节OA诊断准确性的潜力。然而，学习鉴别属性可以是一个具有挑战性的任务，特别是在处理诸如X射线图像的复杂数据时，通常用于膝关节OA诊断。在本文中，我们介绍了一种辨别性正则化的自动编码器（DRAE），允许学习提高分类性能的相关和辨别性质。更具体地，称为判别损失的惩罚项与标准的自动编码器训练标准相结合。此额外阶段旨在强制学习的表示来包含歧视信息。我们对公共多中心骨关节炎倡议（OAI）的数据的实验结果表明，开发方法为早期膝关节OA检测提供了潜在的结果。

著录项

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