Semi-supervised CycleGAN for domain transformation of chest CT images and its application to opacity classification of diffuse lung diseases.

PURPOSE: The performance of deep learning may fluctuate depending on the imaging devices and settings. Although domain transformation such as CycleGAN for normalizing images is useful, CycleGAN does not use information on the disease classes. Therefore, we propose a semi-supervised CycleGAN with an additional classification loss to transform images suitable for the diagnosis. The method is evaluated by opacity classification of chest CT. METHODS: (1) CT images taken at two hospitals (source and target domains) are used. (2) A classifier is trained on the target domain. (3) Class labels are given to a small number of source domain images for semi-supervised learning. (4) The source domain images are transformed to the target domain. (5) A classification loss of the transformed images with class labels is calculated. RESULTS: The proposed method showed an F-measure of 0.727 in the domain transformation from hospital A to B, and 0.745 in that from hospital B to A, where significant differences are between the proposed method and the other three methods. CONCLUSIONS: The proposed method not only transforms the appearance of the images but also retains the features being important to classify opacities, and shows the best precision, recall, and F-measure.

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns.

PURPOSE: 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. METHODS: A large number of frequently appeared patterns of opacities are extracted by a data mining algorithm named genetic network programming (GNP), and the extracted patterns are automatically distributed to several clusters using genetic algorithm (GA). In this paper, lung CT images are used to make clusters of normal and diffuse lung diseases. RESULTS: After executing the pattern extraction by GNP, 1,148 frequent attribute patterns were extracted; then, GA was executed to make clusters. This paper deals with making clusters of normal and five kinds of abnormal opacities (i.e., six-class problem), and then, the proposed method without using correct class labels in the training showed 47.7 % clustering accuracy. CONCLUSION: It is clarified that the proposed method can make clusters without using correct labels and has the potential to apply to CAD, reducing the time cost for labeling CT images.