Multiple-instance learning with global and local features for thyroid ultrasound image classification

著录项

• 来源
  《International Conference on BioMedical Engineering and Informatics》|2014年||共5页
• 会议地点
• 作者

  Jianrui Ding; Cheng H.D.; Jianhua Huang; Yingtao Zhang;

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• 作者单位

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• 会议组织
• 原文格式 PDF
• 正文语种
• 中图分类 生物医学工程;
• 关键词

  biomedical ultrasonics; elasticity; feature extraction; image classification; learning (artificial intelligence); medical image processing; self-organising feature maps; support vector machines; SVM; bag vector; elasticity information; elastogram hue component; global elasticity; global feature; lesion; local feature; multimodality thyroid ultrasound image; multiple-instance learning; self-organizing map; supervised learning method; support vector machine; thyroid B-mode ultrasound image; thyroid ultrasound image classification; Accuracy; Cancer; Elasticity; Feature extraction; Lesions; Support vector machines; Ultrasonic imaging; Multiple-instance learning (MIL); classification; thyroid;

  机译：生物医学超声波;弹性;特征提取;图像分类;学习（人工智能）;医学图像处理;自组织特征图;支持向量机;SVM;袋子矢量;弹性信息;全局弹性;全球弹性;全球特征;损伤;局部特征;多模甲状腺超声图像;多实例学习;自组织地图;监督学习方法;支持向量机;甲状腺B模式超声图像;甲状腺超声图像分类;准确性;癌症;特征提取;特征提取;病变;支持向量机;超声波成像;多实例学习（MIL）;分类;甲状腺;