  The upheaval brought by the arrival of the COVID-19 pandemic has continued to
bring fresh challenges over the past two years. During this COVID-19 pandemic,
there has been a need for rapid identification of infected patients and
specific delineation of infection areas in computed tomography (CT) images.
Although deep supervised learning methods have been established quickly, the
scarcity of both image-level and pixel-level labels as well as the lack of
explainable transparency still hinder the applicability of AI. Can we identify
infected patients and delineate the infections with extreme minimal
supervision? Semi-supervised learning has demonstrated promising performance
under limited labelled data and sufficient unlabelled data. Inspired by
semi-supervised learning, we propose a model-agnostic calibrated
pseudo-labelling strategy and apply it under a consistency regularization
framework to generate explainable identification and delineation results. We
demonstrate the effectiveness of our model with the combination of limited
labelled data and sufficient unlabelled data or weakly-labelled data. Extensive
experiments have shown that our model can efficiently utilize limited labelled
data and provide explainable classification and segmentation results for
decision-making in clinical routine. The code is available at
https://github.com/ayanglab/XAI COVID-19.
