Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns
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- 作者：Shingo Mabu ; Masanao Obayashi…
- 关键词：Computer ; aided diagnosis ; Diffuse lung diseases ; Unsupervised learning ; Clustering ; Data mining ; Evolutionary computation
- 刊名：International Journal of Computer Assisted Radiology and Surgery
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：12
- 期：3
- 页码：519-528
- 全文大小：
- 刊物主题：Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general;
- 出版者：Springer International Publishing
- ISSN：1861-6429
- 卷排序：12
文摘
PurposeFor realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifier, a large number of images with correct labels are necessary. It is a very time-consuming and impractical task for radiologists to give correct labels for a large number of CT images. In this paper, to solve the above problem and realize an unsupervised class labeling mechanism without using correct labels, a new clustering algorithm for diffuse lung diseases using frequent attribute patterns is proposed.