Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto, Noriaki Hashimoto, Yasushi Hirano, Shoji Kido

Abstract: For 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.