Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

• URL: http://arxiv.org/abs/2202.07422v1
• Date: Fri, 11 Feb 2022 17:32:46 GMT
• Title: Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
• Authors: Ming Li, Yingying Fang, Zeyu Tang, Chibudom Onuorah, Jun Xia, Javier Del Ser, Simon Walsh, Guang Yang
• Abstract summary: We propose a model-agnostic calibrated pseudo-labelling strategy 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.
• Score: 13.022429824742055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 pixellevel 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 (SSL) has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by SSL, 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.

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