Copd classification with machine-trained abnormality detection

Abstract

For COPD classification in a medical imaging system, machine learning is used to learn to classify whether a patient has COPD. An image-to-image network deep learns spatial features indicative of various or any type of COPD. The pulmonary function test may be used as the ground truth in training the features and classification from the spatial features. Due to the high availability of pulmonary function test results and corresponding CT scans, there are many training samples. Values from learned features of the image-to-image network are then used to create a spatial distribution of level of COPD, providing information useful for distinguishing between types of COPD without requiring ground truth annotation of spatial distribution of COPD in the training.