Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection
Jianpeng Zhang,Yutong Xie,Guansong Pang,Zhibin Liao,Johan W. Verjans,Wenxing Li,Zongji Sun,Jian He,Yi Li,Chunhua Shen,Yong Xia +10 more
TLDR
The proposed CAAD model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases and achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.Abstract:
Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case ( i.e. , viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.read more
Citations
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Journal ArticleDOI
Deep learning-enabled medical computer vision.
Andre Esteva,Katherine Chou,Serena Yeung,Nikhil Naik,Ali Madani,Ali Mottaghi,Yun Liu,Eric J. Topol,Jeffrey Dean,Richard Socher +9 more
TL;DR: In this paper, the authors survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment.
Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records
Huijun Chen,Juanjuan Guo,Chen Wang,Fan Luo,Xuechen Yu,Wei Zhang,Jiafu Li,Dongchi Zhao,Dan Xu,Qing Gong,Jing Liao,Huixia Yang,Wei Hou,Yuanzhen Zhang +13 more
TL;DR: There is currently no evidence for intrauterine infection caused by vertical transmission in women who develop COVID-19 pneumonia in late pregnancy, according to this small group of cases.
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Applications of artificial intelligence in battling against covid-19: A literature review.
TL;DR: An overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc.
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Deep learning for chest X-ray analysis: A survey.
TL;DR: In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.
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A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.
Jawad Rasheed,Akhtar Jamil,Alaa Ali Hameed,Usman Aftab,Javaria Aftab,Syed Attique Shah,Dirk Draheim +6 more
TL;DR: This survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19, and investigates Artificial Intelligence approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing.
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