COVID-19 Image Data Collection: Prospective Predictions Are the Future

Abstract

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset

Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org. Code for baseline experiments can be found here: https://github.com/mlmed/covid-baselines

References in corpus (15)

• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• COVID-19 Image Data Collection
• COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images
• Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
• Few-Shot Adversarial Domain Adaptation
• Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
• On the limits of cross-domain generalization in automated X-ray prediction
• COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
• Lung Segmentation from Chest X-rays using Variational Data Imputation
• TorchXRayVision: A library of chest X-ray datasets and models
• COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
• Explanation by Progressive Exaggeration
• Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

Cited by in corpus (31)

• Deep Learning for Chest X-ray Analysis: A Survey
• Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
• Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports
• FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
• Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
• DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
• Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data
• Self-supervised deep convolutional neural network for chest X-ray classification
• Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis
• Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image
• Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets
• Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
• CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images
• MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
• Spectral decoupling allows training transferable neural networks in medical imaging
• A methodology for training homomorphicencryption friendly neural networks
• Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
• Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models
• Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness
• COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images
• Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease
• Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach
• Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
• Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
• POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
• An In-depth Review of Privacy Concerns Raised by the COVID-19 Pandemic
• Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
• CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning