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

TL;DR

This paper introduces a semi-supervised, explainable AI approach for identifying and delineating COVID-19 infections in CT images using calibrated pseudo-labels, effectively working with limited labeled data.

Contribution

It proposes a novel model-agnostic calibrated pseudo-labeling strategy within a consistency regularization framework for explainable infection detection with minimal supervision.

Findings

Effective utilization of limited labeled data with unlabelled data

Provides explainable classification and segmentation results

Achieves promising performance in COVID-19 infection delineation

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 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.