  Despite tremendous efforts, it is very challenging to generate a robust model
to assist in the accurate quantification assessment of COVID-19 on chest CT
images. Due to the nature of blurred boundaries, the supervised segmentation
methods usually suffer from annotation biases. To support unbiased lesion
localisation and to minimise the labeling costs, we propose a data-driven
framework supervised by only image-level labels. The framework can explicitly
separate potential lesions from original images, with the help of a generative
adversarial network and a lesion-specific decoder. Experiments on two COVID-19
datasets demonstrate the effectiveness of the proposed framework and its
superior performance to several existing methods.
