Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learning

Abstract

Research on computer-aided diagnosis (CAD) for medical images using machine learning has been actively conducted. However, machine learning, especially deep learning, requires a large number of training data with annotations. Deep learning often requires thousands of training data, but it is tough work for radiologists to give normal and abnormal labels to many images. In this research, aiming the efficient opacity annotation of diffuse lung diseases, unsupervised and semi-supervised opacity annotation algorithms are introduced. Unsupervised learning makes clusters of opacities based on the features of the images without using any opacity information, and semi-supervised learning efficiently uses the small number of training data with annotation for training classifiers. The performance evaluation is carried out by clustering or classification of six kinds of opacities of diffuse lung diseases in computed tomography (CT) images: consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal, and the effectiveness of the proposed methods is clarified.