Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）
Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety. We propose COVID-Net UV, an end-to-end hybrid spatio-temporal deep neural network architecture, to detect COVID-19 infection from lung point-of-care ultrasound videos captured by convex transducers. COVID-Net UV comprises a convolutional neural network that extracts spatial features and a recurrent neural network that learns temporal dependence. After careful hyperparameter tuning, the network ac…
The Ohio State University（俄亥俄州立大学）；The University of Arizona（亚利桑那大学）
The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environments and third world countries. Our co…
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
University of Waterloo（滑铁卢大学）；National Research Council Canada（加拿大国家研究委员会）；McGill University（麦吉尔大学）
University of Waterloo（滑铁卢大学）；National Research Council Canada（加拿大国家研究委员会）；Oakville Trafalgar Memorial Hospital（奥克维尔特拉法加纪念医院）；McMaster University（麦克马斯特大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）
The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus. Point-of-care ultrasound (POCUS) imaging has been proposed as a screening tool as it is a much cheaper and easier to apply imaging modality than others that are traditionally used for pulmonary examinations, namely chest x-ray and computed tomography. Given the scarcity of expert ra…
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the high stakes application we propose the use of robust and explainable techniques. We demonstrate experimentally that robust models have more stable predictions and offer improved interpretability. A framework of contrastive explanations based…
The Coronavirus Disease 2019 (COVID-19) has spread globally and caused serious damage. Chest X-ray images are widely used for COVID-19 diagnosis and the Artificial Intelligence method can increase efficiency and accuracy. In the Challenge of Chest XR COVID-19 detection in Ethics and Explainability for Responsible Data Science (EE-RDS) conference 2021, we proposed a method that combined Swin Transformer and Transformer in Transformer to classify chest X-ray images as three classes: COVID-19, Pneumonia, and Normal (healthy) and achieved 0.9475 accuracies on the test set.
University of Waterloo（滑铁卢大学）；Waterloo AI Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（达尔文人工智能公司）
Computed tomography (CT) has been widely explored as a COVID-19 screening and assessment tool to complement RT-PCR testing. To assist radiologists with CT-based COVID-19 screening, a number of computer-aided systems have been proposed. However, many proposed systems are built using CT data which is limited in both quantity and diversity. Motivated to support efforts in the development of machine learning-driven screening systems, we introduce COVIDx CT-3, a large-scale multinational benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices f…
University of Manouba（马努巴大学）；Taibah University（塔伊巴大学）
By the start of 2020, the novel coronavirus disease (COVID-19) has been declared a worldwide pandemic. Because of the severity of this infectious disease, several kinds of research have focused on combatting its ongoing spread. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this context, Convolutional Neural Networks (CNNs) are presented as efficient techniques for early diagnosis. In this study, we propose a novel randomly initialized CNN architecture for the recognition of COVID-19. This network consists of a set of differ…
We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes …
Kobe University Graduate School of Medicine（神户大学医学院）；Kyoto University Graduate School of Medicine（京都大学医学院）
Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. Materials and Methods: From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were …
National Center for Biotechnology Information (NCBI)（美国国家生物技术信息中心）；National Library of Medicine (NLM)（美国国家医学图书馆）；National Institutes of Health (NIH)（美国国立卫生研究院）；Weill Cornell Medicine（威尔康奈尔医学院）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure…
Shiraz University（设拉子大学）；Lancaster University（兰卡斯特大学）；Tehran University of Medical Sciences（德黑兰医科大学）；Bushehr University of Medical Sciences（布什尔医科大学）
The rapid outbreak of COVID-19 threatens humans life all around the world. Due to insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. As chest radiography, such as chest X-ray (CXR) and CT computed tomography (CT), is a possible way for screening COVID-19, developing an automatic image classification tool is immensely helpful for detecting the patients with COVID-19. To date, researchers have proposed several different screening methods; however, none of them could achieve a reliable and highly sensitive per…
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection …
University of Waterloo（滑铁卢大学）；Waterloo AI Institute（滑铁卢人工智能研究院）；DarwinAI Corp.（达尔文人工智能公司）
After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained…
Sabaragamuwa University of Sri Lanka（斯里兰卡萨伯勒格穆沃大学）
The novel coronavirus variant, which is also widely known as COVID-19, is currently a common threat to all humans across the world. Effective recognition of COVID-19 using advanced machine learning methods is a timely need. Although many sophisticated approaches have been proposed in the recent past, they still struggle to achieve expected performances in recognizing COVID-19 using chest X-ray images. In addition, the majority of them are involved with the complex pre-processing task, which is often challenging and time-consuming. Meanwhile, deep networks are end-to-end and have shown promisin…
Shahid Bahonar University of Kerman（克尔曼沙希德·巴霍纳尔大学）
Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep learning methods have been considered a fast and accurate method. In this paper, by the parallel combination of three well-known pre-trained networks, we attempted to distinguish coronavirus-infected samples from healthy samples. The negative log-likelihood loss function has been used for model training. CT scan images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2 dataset contains 2482 images of lung CT scans, of which 1252 images belong to COVID-19-infected samples. The proposed model was close to …
National Institute of Technology Delhi（德里国家理工学院）；Indian Institute of Technology, Roorkee（鲁尔基印度理工学院）
Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical facilities are limited, it is recommended to diagnose patients as per the severity of the infection. Keeping this in mind, we share our research in detecting COVID-19 as well as assessing its severity using chest-CT scans and Deep Learning…
Andalusian Research Institute in Data Science and Computational Intelligence（安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）；University of Vigo（维戈大学）；University of Alicante（阿利坎特大学）
Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allo…
The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the number of cases has put considerable strain on health care systems around the world. A critical step in the treatment and management of COVID-19 positive patients is severity assessment, which is challenging even for expert radiologists given the subtleties at different stages of lung disease severity. Motivated by this challenge, we introduce COVID-Net CT-S, a suite of deep convolutional neural networks for predicting l…
University of Engineering and Applied Science（工程与应用科学大学）
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is deve…
National Institute of Technology Delhi（德里国家技术学院）；Indian Institute of Technology, Roorkee（印度理工学院鲁尔基分校）
Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 …
Middlesex University（密德萨斯大学）；Newham University Hospital（纽汉大学医院）；Barts Health NHS Trust（巴茨健康国民医疗服务信托）
This paper is responding to the MIA-COV19 challenge to classify COVID from non-COVID based on CT lung images. The COVID-19 virus has devastated the world in the last eighteen months by infecting more than 182 million people and causing over 3.9 million deaths. The overarching aim is to predict the diagnosis of the COVID-19 virus from chest radiographs, through the development of explainable vision transformer deep learning techniques, leading to population screening in a more rapid, accurate and transparent way. In this competition, there are 5381 three-dimensional (3D) datasets in total, incl…
The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and r…
Problem: Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Aim: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Meth…
Ferdowsi University of Mashhad（马什哈德费尔多西大学）；University of Kufa（库法大学）
Propose: Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world's population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of…
São Paulo State University (UNESP)（圣保罗州立大学（UNESP））
COVID-19 quickly became a global pandemic after only four months of its first detection. It is crucial to detect this disease as soon as possible to decrease its spread. The use of chest X-ray (CXR) images became an effective screening strategy, complementary to the reverse transcription-polymerase chain reaction (RT-PCR). Convolutional neural networks (CNNs) are often used for automatic image classification and they can be very useful in CXR diagnostics. In this paper, 21 different CNN architectures are tested and compared in the task of identifying COVID-19 in CXR images. They were applied t…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Niagara Health（尼亚加拉医疗）；McMaster University（麦克马斯特大学）；Thunder Bay Regional Health Sciences Centre（桑德贝地区健康科学中心）；Northern Ontario School of Medicine（北部安大略医学院）
The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolution…
With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.
University of Science and Technology Beijing（北京科技大学）
In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network and transformer together for the detection of COVID-19 via 3D chest CT images. It consists of a CNN feature extractor module with SE attention to extract sufficient features from CT scans, together with a transformer model to model the discriminative features of the 3D CT scans. Compared to previous works, CTNet provides an effective and efficient method to perform COVID-19 diagnosis via 3D CT scans with data resampling strategy. Advanced results on a large and public benchmarks, …
Rutgers University（罗格斯大学）；Rutgers New Jersey Medical School（罗格斯新泽西医学院）；Rutgers Cancer Institute of New Jersey（罗格斯新泽西癌症研究所）；Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院）
Recently, the outbreak of the novel Coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost and portability gains much attention and becomes very promising. Computational methods with high accurac…
Concordia Institute for Information Systems Engineering（康考迪亚大学信息系统工程研究所）；Concordia University（康考迪亚大学）
Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis metho…
COVID-19 has led to hundreds of millions of cases and millions of deaths worldwide since its onset. The fight against this pandemic is on-going on multiple fronts. While vaccinations are picking up speed, there are still billions of unvaccinated people. In this fight against the virus, diagnosis of the disease and isolation of the patients to prevent any spread play a huge role. Machine Learning approaches have assisted in the diagnosis of COVID-19 cases by analyzing chest X-rays and CT-scan images of patients. To push algorithm development and research in this direction of radiological diagno…
Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namel…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（达尔文人工智能公司）
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients with worsening respiratory status or developing complications tha…
Intelligent Medical Image Computing Systems (IMICS) Lab（智能医学影像计算系统实验室）；The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction (RT-PCR) tests. Supervised deep learning models such as convolutional neural networks (CNN) need enough labeled data for all classes to correct…
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（达尔文人工智能公司）
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. T…
Beijing University of Posts and Telecommunications（北京邮电大学）
The COVID-19 epidemic has become a major safety and health threat worldwide. Imaging diagnosis is one of the most effective ways to screen COVID-19. This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model. The model validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall, and correctly classifies 98 out of 100 COVID-19 x-ray images in test set with more than 81% prediction probability under the condition of 95% conf…
Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. In the absence of specific drugs or vaccines for the treatment of COVID-19 and the limitation of prevailing diagnostic techniques, there is a requirement for some alternate automatic screening systems that can be used by the physicians to quickly identify and isolate the infected patients. A chest X-ray (CXR) image can be used as an alternative modality to detect and diagnose the COVID-19. In this stu…
The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from chest X-ray images. Our method can use self-knowledge of images based on similarities of their visual features for self-supervised learning. Experimental results show that our method achieved an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open COVID-19…
University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Qatar Foundation（卡塔尔基金会）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；College of Medicine（医学院）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）
The use of computer-aided diagnosis in the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal…
Early identification of COVID-19 using a deep model trained on Chest X-Ray and CT images has gained considerable attention from researchers to speed up the process of identification of active COVID-19 cases. These deep models act as an aid to hospitals that suffer from the unavailability of specialists or radiologists, specifically in remote areas. Various deep models have been proposed to detect the COVID-19 cases, but few works have been performed to prevent the deep models against adversarial attacks capable of fooling the deep model by using a small perturbation in image pixels. This paper…
Radiological image is currently adopted as the visual evidence for COVID-19 diagnosis in clinical. Using deep models to realize automated infection measurement and COVID-19 diagnosis is important for faster examination based on radiological imaging. Unfortunately, collecting large training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for COVID-19 diagnosis from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, cal…
Indian Institute of Information Technology, Sri City, Chittoor（印度信息技术学院，斯里城，奇图尔）；Indian Institute of Information Technology, Allahabad（印度信息技术学院，阿拉哈巴德）
Covid-19 global pandemic continues to devastate health care systems across the world. At present, the Covid-19 testing is costly and time-consuming. Chest X-Ray (CXR) testing can be a fast, scalable, and non-invasive method. The existing methods suffer due to the limited CXR samples available from Covid-19. Thus, inspired by the limitations of the open-source work in this field, we propose attention guided contrastive CNN architecture (AC-CovidNet) for Covid-19 detection in CXR images. The proposed method learns the robust and discriminative features with the help of contrastive loss. Moreover…
HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation
Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan University of Technology（伊斯法罕理工大学）；Arak University of Medical Sciences（阿拉克医科大学）；Islamic Azad University（伊斯兰阿扎德大学）
Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）
Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other han…
Unidad Mixta de Imagen Biomédica FISABIO-CIPF（FISABIO-CIPF生物医学成像联合单元）；Universidad de Alicante（阿利坎特大学）；Medbravo；Hospital San Juan de Alicante（阿利坎特圣胡安医院）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）；Sierra Research SL（Sierra Research有限公司）；Bioinformatics & Biostatistics Unit, Principe Felipe Research Center（费利佩王子研究中心生物信息学与生物统计学单元）
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerab…
Maulana Azad National Institute of Technology（毛拉纳·阿扎德国家理工学院）
Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtain…
A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain Reaction), was introduced. It collects swab specimens from the patient's nose or throat, where the COVID-19 virus gathers. This metho…
The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. Automatic identification of COVID-19 is a challenge for health care officials. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Some limitations encountered while collecting swabs are related to accuracy and longtime duration. Chest CT (Computed Tomography) is another test method that helps healthcare providers quickly identify the infected lung areas. It was used as a supporting tool for identifying CO…
Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
Novel Coronavirus disease (COVID-19) is an extremely contagious and quickly spreading Coronavirus infestation. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep Convolutional Neural Networks (CNNs). A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition syste…
Amirkabir University of Technology（阿米尔卡比尔理工大学）；Semnan University（塞姆南大学）
In late 2019 and after COVID-19 pandemic in the world, many researchers and scholars have tried to provide methods for detection of COVID-19 cases. Accordingly, this study focused on identifying COVID-19 cases from chest X-ray images. In this paper, a novel approach to diagnosing coronavirus disease from X-ray images was proposed. In the proposed method, DenseNet169 deep neural network was used to extract the features of X-ray images taken from the patients' chest and the extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm so that it could perform t…
We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. We find that the synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. We also find that comparable performance can also be achi…
Università degli Studi di Milano（米兰大学）；ASST Grande Ospedale Metropolitano Niguarda（ASST 大都会大医院尼瓜达）；INFN Sezione di Milano（意大利国家核物理研究院米兰分部）
Purpose: to optimize a pipeline of clinical data gathering and CT images processing implemented during the COVID-19 pandemic crisis and to develop artificial intelligence model for different of viral pneumonia. Methods: 1028 chest CT image of patients with positive swab were segmented automatically for lung extraction. A Gaussian model developed in Python language was applied to calculate quantitative metrics (QM) describing well-aerated and ill portions of the lungs from the histogram distribution of lung CT numbers in both lungs of each image and in four geometrical subdivision. Furthermore,…
University of Saskatchewan（萨斯喀彻温大学）；International Road Dynamics（国际道路动力学公司）；National Institute of Technology, Trichy（特里奇国家理工学院）
One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper,…
Prairie View A&M University, Texas A&M University System（德州农工大学系统草原风光农工大学）
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 X-ray 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 ou…
University of South Florida（南佛罗里达大学）；University of California, San Francisco（加州大学旧金山分校）
Identifying who is infected with the Covid-19 virus is critical for controlling its spread. X-ray machines are widely available worldwide and can quickly provide images that can be used for diagnosis. A number of recent studies claim it may be possible to build highly accurate models, using deep learning, to detect Covid-19 from chest X-ray images. This paper explores the robustness and generalization ability of convolutional neural network models in diagnosing Covid-19 disease from frontal-view (AP/PA), raw chest X-ray images that were lung field cropped. Some concerning observations are made…
COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and o…
