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, noninvasive, 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 Preprint. Under review.

The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images

“…There have recently been many remarkable efforts to exploit machine learning tools to facilitate the analysis of images for the diagnosis of COVID-19. These efforts include initiatives to collect worldwide images and make them public [26][27][28][29][30] as well as attempts to automate the analysis of CT scans or lung X-ray images. Mazzilli et al [15] developed a method for automatically evaluating lung conditions using CT scans to determine COVID-19 infection.…”

“…In this work, we collected images from all the relevant sources that we could find [26,28,29,[53][54][55]. Since the number of available images has been continually increasing, we have added more and more images to our model but also had to discard a significant number of them due to constraints that will be elaborated on in Section 2.…”

“…However, there are a few highly valuable initiatives to make images publicly available through repositories for practitioners. The images used in this work were extracted from the following sources: [26,28,29,[53][54][55]. Table 1 lists the number of images extracted from each source.…”

“…Label Details [28,29] 542 217 168 images labeled as "COVID-19" 12 images labeled as "No Finding" 3 images labeled as "Legionella" 6 images labeled as "SARS (no COVID-19)" 6 images labeled as "Pneumocystis" 6 images labeled as "Streptococcus" 1 image labeled as "Chlamydophila" 3 images labeled as "Escherichia Coli" 5 images labeled as "Klebsiella" 2 images labeled as "Pneumonia" 3 images labeled as "Varicella" 1 image labeled as "Lipoid" 1 image labeled as "Influenza"…”

Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring

“…There have been important recent efforts to push for open access and open source solutions for CXR-driven COVID-19 case detection and classification [29,[43][44][45][46]. Among these datasets, COVID-Net [29], which is considered as one of the largest CXR datasets for the pandemic study, leverages the human-machine collaborative design strategy to conduct the dataset.…”