CAGAN: Classifier-augmented generative adversarial networks for weakly-supervised COVID-19 lung lesion localisation

Abstract

The Coronavirus Disease 2019 (COVID-19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID-19 patients. The authors propose a classifier-augmented generative adversarial network framework for weakly supervised COVID-19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo-healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre-trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high-level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID-19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods.