zurlog/subset_wlabels huggingface dataset:
Title: ai-driven covid-19 tools to interpret quantify lung images
Journal: IEEE Pulse
Published: 2020-08-18 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19
Task: Review | Modality: Multimodal

Abstract: Qualitative interpretation is a good thing when it comes to reading lung images in the fight against coronavirus 2019 disease (COVID-19), but quantitative analysis makes radiology reporting much more comprehensive. To that end, several research groups have begun looking to artificial intelligence (AI) as a tool for reading and analyzing X-rays and computed tomography (CT) scans, and helping to diagnose and monitor COVID-19.
Title: automatic analysis system of covid-19 radiographic lung images (xraycovidetector)
Published: 2020-08-23 (Preprint, medRxiv)
Condition: COVID-19;Communicable Diseases;Cough;Dyspnea;Fatigue;Fever;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: COVID-19 is a pandemic infectious disease caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever, dry cough, dyspnea, fatigue, pneumonia, and radiological manifestations. The most common reported RX and CT findings include lung consolidation and ground-glass opacities. In this paper, we describe a machine learning-based system (XrayCoviDetector; until the image has a size ), that detects automatically, the probability that a thorax radiological image includes COVID-19 lung patterns. XrayCoviDetector has an accuracy of 0.93, a sensitivity of 0.96, and a specificity of 0.90.
Title: current landscape of imaging and the potential role for artificial intelligence in the management of covid-19
Journal: Curr Probl Diagn Radiol
Published: 2020-07-25 (Journal Article;Review, Peer reviewed (PubMed))
Condition: COVID-19
Task: Review | Modality: Multimodal

Abstract: The clinical management of COVID-19 is challenging. Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of this disease. Chest x-ray radiography and computed tomography) are the standard imaging modalities used for the structural assessment of the disease status, while functional imaging (namely, positron emission tomography) has had limited application. Artificial intelligence can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. We review the current landscape of these imaging modalities and artificial intelligence approaches as applied in COVID-19 management.
Title: research on cnn-based models optimized by genetic algorithm and application in the diagnosis of pneumonia and covid-19
Published: 2020-04-25 (Preprint, medRxiv)
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: In this research, an optimized deep learning method was proposed to explore the possibility and practicality of neural network applications in medical imaging. The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to optimize the Dropout module, which is essential in neural networks so as to improve the performance of typical neural network models. The experiment results demonstrate that the proposed method shows excellent performance and strong practicability in judging pneumonia, and the application of advanced artificial intelligence technology in the field of medical imaging has broad prospects.
Title: attention u-net based adversarial architectures for chest x-ray lung segmentation
Published: 2020-03-23 (Preprint, arXiv)
Condition: COVID-19;Lung Cancer;Pneumonia;Tuberculosis
Task: Segmentation-only | Modality: X-Ray

Abstract: Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.
Title: using deep convolutional neural networks to diagnose covid-19 from chest x-ray images
Published: 2020-07-19 (Preprint, arXiv)
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: 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% confidence interval. This project may serve as a reference for other researchers aiming to advance the development of deep learning applications in medical imaging.
Title: deep learning covid-19 features on cxr using limited training data sets
Journal: IEEE Trans Med Imaging
Published: 2020-05-13 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;COVID-19 Pandemic
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
Title: transfer learning for covid-19 pneumonia detection and classification in chest x-ray images
Published: 2020-12-16 (Preprint, medRxiv)
Condition: COVID-19;Infections;Pneumonia;Pneumonia, Bacterial;Pneumonia, Viral
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.
Title: new machine learning method for image-based diagnosis of covid-19
Journal: PLoS One
Published: 2020-06-27 (Journal Article;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.
Title: survey of the detection and classification of pulmonary lesions via ct and x-ray
Published: 2020-12-30 (Preprint, arXiv)
Condition: COVID-19;COVID-19 Pandemic;Lung Diseases;Pneumonia
Task: Review | Modality: Multimodal

Abstract: In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. Furthermore, this review introduces 26 commonly used public medical image datasets, summarizes the latest technology, and discusses current challenges and future research directions.
Title: an empirical study on detecting covid-19 in chest x-ray images using deep learning based methods
Published: 2020-10-10 (Preprint, arXiv)
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Spreading of COVID-19 virus has increased the efforts to provide testing kits. Not only the preparation of these kits had been hard, rare, and expensive but also using them is another issue. Results have shown that these kits take some crucial time to recognize the virus, in addition to the fact that they encounter with 30% loss. In this paper, we have studied the usage of x-ray pictures which are ubiquitous, for the classification of COVID-19 chest Xray images, by the existing convolutional neural networks (CNNs). We intend to train chest x-rays of infected and not infected ones with different CNNs architectures including VGG19, Densnet-121, and Xception. Training these architectures resulted in different accuracies which were much faster and more precise than usual ways of testing.
Title: covid-19 in cxr: from detection and severity scoring to patient disease monitoring
Journal: IEEE J Biomed Health Inform
Published: 2021-03-27 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Disease Progression;Pneumonia
Task: 3. Monitoring/Severity assessment | Modality: X-Ray

Abstract: This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.
Title: coronavirus detection and analysis on chest ct with deep learning
Published: 2020-04-06 (Preprint, arXiv)
Condition: COVID-19;Death
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
Title: automatic detection of covid-19 infection from chest x-ray using deep learning
Published: 2020-05-14 (Preprint, medRxiv)
Condition: COVID-19;Infections
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the prevailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.
Title: explainable dcnn based chest x-ray image analysis and classification for covid-19 pneumonia detection
Journal: Sci Rep
Published: 2021-08-11 (Journal Article;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
Title: early detection of coronavirus cases using chest x-ray images employing machine learning and deep learning approaches
Published: 2020-06-08 (Preprint, medRxiv)
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy , AUC , f-measure , sensitivity and specificity as well as the lowest fall out and miss rate respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.
Title: machine learning and image analysis applications in the fight against covid-19 pandemic: datasets research directions challenges and opportunities
Journal: Mater Today Proc
Published: 2020-09-29 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19 Pandemic
Task: Review | Modality: X-Ray

Abstract: COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread and case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.
Title: a cascaded learning strategy for robust covid-19 pneumonia chest x-ray screening
Published: 2020-04-30 (Preprint, arXiv)
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.
Title: covid-classifier: an automated machine learning model to assist in the diagnosis of covid-19 infection in chest x-ray images
Journal: Sci Rep
Published: 2021-05-12 (Journal Article;Research Support, N.I.H., Extramural;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19;Infections;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
Title: automated triage of covid-19 from various lung abnormalities using chest ct features
Published: 2020-10-24 (Preprint, arXiv)
Condition: COVID-19;Infections;Pneumonia
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.
Title: distinguishing l and h phenotypes of covid-19 using a single x-ray image
Published: 2020-05-03 (Preprint, medRxiv)
Condition: COVID-19;Disease Progression;Pneumonia;Respiratory Tract Diseases
Task: 4. Prognosis/Treatment | Modality: X-Ray

Abstract: Recent observations have shown that there are two types of COVID-19 response: an H phenotype with high lung elastance and weight, and an L phenotype with low measures1. H-type patients have pneumonia-like thickening of the lungs and require ventilation to survive; L-type patients have clearer lungs that may be injured by mechanical assistance2,3. As treatment protocols differ between the two types, and the number of ventilators is limited, it is vital to classify patients appropriately. To date, the only way to confirm phenotypes is through high-resolution computed tomography2. Here, we identify L- and H-type patients from their frontal chest x-rays using feature-embedded machine learning. We then apply the categorization to multiple images from the same patient, extending it to detect and monitor disease progression and recovery. The results give an immediate criterion for coronavirus triage and provide a methodology for respiratory diseases beyond COVID-19.
Title: covid-19 detection from chest x-ray images using deep learning and convolutional neural networks
Published: 2020-09-02 (Preprint, medRxiv)
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Accurate and efficient diagnosis of potential COVID-19 patients is vital in the fight against the current pandemic. However, even the gold-standard COVID-19 test, reverse transcription polymerase chain reaction, suffers from a high false negative rate and a turnaround time of up to one week, preventing the infected from accessing the timely care they require, and impeding efforts to isolate positive cases. To address these shortcomings, this study develops a machine learning model based on the DenseNet-201 deep convolutional neural network, that can classify COVID-19 from chest radiographs in less than one minute and far more accurately than conventional tests (F1-score: 0.96; precision: 0.95; recall: 0.98). It uses a significantly larger dataset and more control classes than previously published models, demonstrating the promise of a machine learning approach for accurate and efficient COVID-19 screening. A live web application of the trained model can be accessed at /.