SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation

Zhou, Jieli; Jing, Baoyu; Wang, Zeya

doi:10.48550/arxiv.2005.11003

Cited by 1 publication

(2 citation statements)

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“…The rationale of these semi-supervised learning models is to enrich the supervision signals by exploiting the knowledge learned on unlabeled data [41], or regularize the network by enforcing smooth and consistent classification boundaries [40]. Regarding COVID-19 research such as COVID-19 image classification and image segmentation, semi-supervised learning is employed to resolve the lacking of labeled data [42], [43], [44], [45], [46], [47]. However, for COVID-19 image classification, these studies [42], [43], [44] have not comprehensively examined the model performance on a large-scale of X-ray image dataset such as COVIDx [20] by comparing with the state-of-the-art, especially for the case of very few labeled data such as less than 10% labeled data.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding COVID-19 research such as COVID-19 image classification and image segmentation, semi-supervised learning is employed to resolve the lacking of labeled data [42], [43], [44], [45], [46], [47]. However, for COVID-19 image classification, these studies [42], [43], [44] have not comprehensively examined the model performance on a large-scale of X-ray image dataset such as COVIDx [20] by comparing with the state-of-the-art, especially for the case of very few labeled data such as less than 10% labeled data. This paper proposed a semi-supervised deep learning model for COVID-19 image classification and checked out the model performance systematically on the COVIDx [20] dataset.…”

Section: Related Workmentioning

confidence: 99%

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Semi-supervised Learning for COVID-19 Image Classification via ResNet

Nwosu¹,

Li²,

Qian³

et al. 2021

Preprint

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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.

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