2020
DOI: 10.1109/tmi.2020.2995508
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Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

Abstract: The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we pr… Show more

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Cited by 350 publications
(283 citation statements)
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“…With the rapid growth rate of COVID-19 suspection all over the world, designing effective automated tools for COVID-19 diagnosis from CT imaging is highly demanded to improve the clinical diagnosis efficiency and release the tedious workload of clinicians and radiologists. However, accurate diagnosis of COVID-19 from CT images is a non-trival problem, mainly due to the highly similar patterns of COVID-19 and other pneumonia types, as well as the large appearance variance of COVID-19 lesions of patients in different severity level [42]. Recently, a variety of data-driven models have been proposed to solve this problem [4], [19], [43], [44], leading to considerable progress in the field of automated COVID-19 diagnosis in the past few months.…”
Section: Discussionmentioning
confidence: 99%
“…With the rapid growth rate of COVID-19 suspection all over the world, designing effective automated tools for COVID-19 diagnosis from CT imaging is highly demanded to improve the clinical diagnosis efficiency and release the tedious workload of clinicians and radiologists. However, accurate diagnosis of COVID-19 from CT images is a non-trival problem, mainly due to the highly similar patterns of COVID-19 and other pneumonia types, as well as the large appearance variance of COVID-19 lesions of patients in different severity level [42]. Recently, a variety of data-driven models have been proposed to solve this problem [4], [19], [43], [44], leading to considerable progress in the field of automated COVID-19 diagnosis in the past few months.…”
Section: Discussionmentioning
confidence: 99%
“…Deep learning has been employed for the diagnosis of COVID-19 in chest CT Song et al (2020); Gozes et al (2020a,b) and community acquired pneumonia (CAP) Kermany et al (2018). For example, Ouyang et al (2020) proposed a 3D convolutional neural network (CNN) with online attention refinement to diagnose COVID-19 from CAP and introduced a sampling strategy to mitigate the imbalanced distribution of infected regions between COVID-19 and CAP. Song et al (2020) proposed DeepPneumonia for localization and detection of COVID-19 pneumonia; attention was also applied to detect key regions with impressive results on a large cohort.…”
Section: Introductionmentioning
confidence: 99%
“…To mitigate the burden on radiologists, while providing the highest quality care for patients, there has been tremendous effort to develop novel image processing approaches using machine learning algorithms 24 , particularly for COVID-19 diagnosis and prognosis 25 . These artificial intelligence (AI) models exploit and build upon medical imaging modalities such as chest CT scans [26][27][28][29][30][31][32] , chest radiographs [33][34][35][36][37][38][39][40] , and lung ultrasound 41 However, for any of these AI models to be useful in assisting clinicians in the care of COVID-19 patients, they require a robust and reliable AI deployment system 42 . Deployment is often a difficult step because clinical radiology infrastructure is not designed for easily embedding third-party systems, and doing so while maintaining context sensitivity and seamlessly embedding such systems into the radiologist workflow requires knowledge of hospital information system integration standards and often product-specific knowledge.…”
Section: Introductionmentioning
confidence: 99%