Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
This work addresses the challenge of accurate lesion quantification for COVID-19 diagnosis, but it is incremental as it builds on existing weakly supervised methods.
The paper tackled the problem of annotation biases in supervised segmentation of COVID-19 lesions on chest CT images by proposing a weakly supervised framework using only image-level labels, which demonstrated superior performance on two datasets.
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.