Novelty detection for breast cancer image classification
作者: Pawel CichoszDariusz JagodzińskiMateusz MatysiewiczŁukasz NeumannRobert M. NowakRafał OkuniewskiWitold Oleszkiewicz
作者单位: Warsaw Univ. of Technology (Poland)
论文集英文名称: Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA)
来源数据库: SPIE-the International Society for Optical Engineering
DOI: 10.1117/12.2249183
原始语种摘要: Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest...
全文获取路径: SPIE 

  • image 
  • applications 应用程序
  • detection 探测
  • unbiased 不偏性
  • classification 分类
  • detecting 检测
  • prediction 预报
  • breast 胸部
  • clustering 聚类
  • create 引起