zurlog/subset_wlabels huggingface dataset:
Title: deep learning and medical image processing for coronavirus (covid-19) pandemic: a survey
Journal: Sustain Cities Soc
Published: 2020-11-11 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;COVID-19 Pandemic;Death;Diabetic Retinopathy
Task: Review | Modality: Multimodal

Abstract: Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.
Title: severity assessment and progression prediction of covid-19 patients based on the lesionencoder framework and chest ct
Published: 2020-08-04 (Preprint, medRxiv)
Condition: COVID-19;Disease Progression;Infections
Task: 4. Prognosis/Treatment | Modality: CT

Abstract: Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression.A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors.Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.
Title: contrastive cross-site learning with redesigned net for covid-19 ct classification
Journal: IEEE J Biomed Health Inform
Published: 2020-09-12 (Journal Article;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19;Infections
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.
Title: uncertainty-driven ensembles of deep architectures for multiclass classification application to covid-19 diagnosis in chest x-ray images
Published: 2020-11-27 (Preprint, arXiv)
Condition: COVID-19;COVID-19 Pandemic;Pneumonia;Pneumonia, Bacterial;Pneumonia, Viral
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Respiratory diseases kill million of people each year. Diagnosis of these pathologies is a manual, time-consuming process that has inter and intra-observer variability, delaying diagnosis and treatment. The recent COVID-19 pandemic has demonstrated the need of developing systems to automatize the diagnosis of pneumonia, whilst Convolutional Neural Network (CNNs) have proved to be an excellent option for the automatic classification of medical images. However, given the need of providing a confidence classification in this context it is crucial to quantify the reliability of the model's predictions. In this work, we propose a multi-level ensemble classification system based on a Bayesian Deep Learning approach in order to maximize performance while quantifying the uncertainty of each classification decision. This tool combines the information extracted from different architectures by weighting their results according to the uncertainty of their predictions. Performance of the Bayesian network is evaluated in a real scenario where simultaneously differentiating between four different pathologies: control vs bacterial pneumonia vs viral pneumonia vs COVID-19 pneumonia. A three-level decision tree is employed to divide the 4-class classification into three binary classifications, yielding an accuracy of 98.06% and overcoming the results obtained by recent literature. The reduced preprocessing needed for obtaining this high performance, in addition to the information provided about the reliability of the predictions evidence the applicability of the system to be used as an aid for clinicians.
Title: a fully automatic deep learning system for covid-19 diagnostic and prognostic analysis
Journal: Eur Respir J
Published: 2020-05-24 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Pneumonia;Pneumonia, Viral
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.
Title: covidgr dataset and covid-sdnet methodology for predicting covid-19 based on chest x-ray images
Journal: IEEE J Biomed Health Inform
Published: 2020-11-11 (Journal Article;Research Support, Non-U.S. Gov't, Peer reviewed (PubMed))
Condition: COVID-19;Communicable Diseases
Task: 3. Monitoring/Severity assessment | Modality: X-Ray

Abstract: 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 allow building such systems as they are highly heterogeneous and biased towards severe cases. This article is three-fold: we demystify the high sensitivities achieved by most recent COVID-19 classification models, under a close collaboration with Hospital Universitario Clínico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 426 positive and 426 negative PA (PosteroAnterior) CXR views and we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of , , in severe, moderate and mild COVID-19 severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 along with the severity level labels are available to the scientific community through this link /.
Title: a chest radiography-based artificial intelligence deep-learning model to predict severe covid-19 patient outcomes: the cape (covid-19 ai predictive engine) model
Published: 2020-05-27 (Preprint, medRxiv)
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Chest radiography may be used together with deep-learning models to prognosticate COVID-19 patient outcomesT o evaluate the performance of a deep-learning model for the prediction of severe patient outcomes from COVID-19 pneumonia on chest radiographs.A deep-learning model (CAPE: Covid-19 AI Predictive Engine) was trained on 2337 CXR images including 2103 used only for validation while training. The prospective test set consisted of CXR images (n=70) obtained from RT-PCR confirmed COVID-19 pneumonia patients between 1 January and 30 April 2020 in a single center. The radiographs were analyzed by the AI model. Model performance was obtained by receiver operating characteristic curve analysis.In the prospective test set, the mean age of the patients was 46 years (84.2% male). The deep-learning model accurately predicted outcomes of ICU admission/mortality from COVID-19 pneumonia with an AUC of 0.79 (95% CI 0.79-0.96). Compared to traditional risk scoring systems for pneumonia based upon laboratory and clinical parameters, the model matched the EWS and MulBTSA risk scoring systems and outperformed CURB-65.A deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 on chest radiographs.A deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 from chest radiographs with an AUC of 0.79, which is comparable to traditional risk scoring systems for pneumonia.This is a chest radiography-based AI model to prognosticate the risk of severe COVID-19 pneumonia outcomes.
Title: deep learning-based recognizing covid-19 and other common infectious diseases of the lung by chest ct scan images
Published: 2020-03-30 (Preprint, medRxiv)
Condition: COVID-19;Communicable Diseases;Pneumonia;Pneumonia, Bacterial;Pneumonia, Viral;Tuberculosis, Pulmonary
Task: 2. Detection/Diagnosis | Modality: CT

Abstract: COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human-based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)-based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung. In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan. Accuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. The performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.
Title: covid-19 infection map generation and detection from chest x-ray images
Journal: Health Inf Sci Syst
Published: 2021-04-08 (Journal Article, Peer reviewed (PubMed))
Condition: COVID-19;Infections
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.
Title: mavidh score: a covid-19 severity scoring using chest x-ray pathology features
Published: 2021-02-02 (Preprint, arXiv)
Condition: COVID-19;Disease Progression
Task: 3. Monitoring/Severity assessment | Modality: X-Ray

Abstract: The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Hence, a simple method based on lung-pathology interpretable features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method correlates well to patient severity in different stages of disease progression with competitive results compared to other existing, more complex methods. An original data selection approach is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-based rather than reliant on lung involvement or opacity as others in the literature. A second contribution comes from the validation of the results, conceptualized as the scoring of patients groups from different stages of the disease. Besides performing such validation on an independent data set, the results were also compared with other proposed scoring methods in the literature. The results show that there is a significant correlation between the scoring system (MAVIDH) and patient outcome, which could potentially help physicians rating and following disease progression in COVID-19 patients.
Title: deep learning for screening covid-19 using chest x-ray images
Published: 2020-08-21 (Preprint, arXiv)
Condition: COVID-19;Pneumonia
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: With the ever increasing demand for screening millions of prospective "novel coronavirus" or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes \textitviz. normal, pneumonia, other\_disease, and Covid-19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 90.13\% . In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.
Title: interpreting deep ensemble learning through radiologist annotations for covid-19 detection in chest radiographs
Published: 2020-07-16 (Preprint, medRxiv)
Condition: COVID-19
Task: 2. Detection/Diagnosis | Modality: X-Ray

Abstract: Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, using these models in medical computer vision tasks suffers from several limitations, viz., adapting to visual characteristics that are unlike natural images; modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; challenges in explaining DL black-box behavior to support clinical decision-making; and inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations for COVID-19 detection using chest X-rays (CXRs). Specifically, our contribution benefits from pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; using ensembles of the fine-tuned models to further improve performance compared to individual constituent models; performing statistical analyses at various learning stages to validate our claims; interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We observe that: ensemble approaches improved classification and localization performance; and, inter-reader variability and performance level assessment helped guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.