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INTRODUCTION: Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. OBJECTIVES: Supervised deep learning dominates COVID-19 pathology data analytics. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events. METHODS: The proposed model with two paths is built based on Residual Neural Network for COVID-19 image classification to reduce labeling efforts, where the two paths refer to a supervised path and an unsupervised path, respectively. RESULTS: Experimental results demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training image. CONCLUSION: The proposed model can reduces the efforts of building deep learning models significantly for COVID-19 image classification.

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Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from Xray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semisupervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.