COVID-19 Image Data Collection

By Joseph Paul Cohen, Paul Morrison, L. Dao. 2020

1,059 citations recorded by the source corpus.

This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, methodology): “IEEE8023 Coivd-chestxray-dataset [26] is an open dataset of COVID-19 cases with chest X-ray and CT images, which allows users to submit other COVID-19 data to this dataset.”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, methodology): “The public dataset of X-ray images was provided by Dr. Joseph Cohen [14] and Dr. Adrian Rosebrock [15] .”
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning (2020, methodology): “These images were extracted from academic publications reporting the results on COVID-19 X-ray and CT images.”
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning (2020, methodology): “The COVID-19 dataset [50] utilized in this research [51] was created by Dr. Joseph Cohen, a postdoctoral fellow at the University of Montreal.”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, positive): “In this study, X-ray images of COVID-19 [41] , and no-ﬁndings and pneumonia [42] were used.”
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images (2020, methodology): “To generate the COVIDx dataset, we combined and modified two different publicly available datasets: 1) COVID-19 image data collection [3], and 2) RSNA Pneumonia Detection Challenge dataset [15].”
AI for radiographic COVID-19 detection selects shortcuts over signal (2020, methodology): “3), which differ in style between the COVID-19-negative and COVID-19-positive datasets, and similarly highlight arrows and other annotations that are uniquely found in the GitHub-COVID data source [17] (Supplementary Fig.”
Transformers in Medical Image Analysis: A Review (2022, neutral): “[45] Chest X-Ray Lung COVID-19 Radiology Dataset [[46], [47]] Transformer × CNN 94.”
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, background): “One X-ray dataset from Cohen et al. [9] contains over-all 122 frontal view X-rays, including 100 images of COVID-19 cases, 11 SARS images, and 11 other pneumonia images.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “Several initiatives exist to crowdsource and open source relevant data, for instance the Covid Chest X-ray Dataset (Cohen et al., 2020) for medical imagery and the COVID-19 Risk Calculator (2020) † for symptoms, but these are challenging to assemble and maintain manually.”
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases (2020, methodology): “h extensive research, a collection of 455 well-visualized, confirmed pathological X-Ray images was created. The final collection includes selected X-Rays from a Github repository created by Dr. Cohen [7], and publically available medical image repositories, such as the Radiological Society of North ”
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets (2020, background): “For comparing COVID-19 from normal and different lung diseases, data were also collected from different sources [22], [23], including additional normal cases.”
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “[74] creates COVID-19 Image Data Collection by assembling medical images from websites and publications, and”