COVID-CT-Dataset: A CT Scan Dataset about COVID-19

By Xingyi Yang, Jinyu Zhao, Yichen Zhang, Xuehai He, Pengtao Xie. 2020

910 citations recorded by the source corpus.

During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbreak of this pandemic. We also perform experimental studies which further demonstrate that this dataset is useful for developing AI-based diagnosis models of COVID-19. Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0.90, an AUC of 0.98, and an accuracy of 0.89. According to the senior radiologist, models with such performance are good enough for clinical usage. The data and code are available at this https URL

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Ahmed Bouridane
2. Ahmad Chaddad
3. Jihao Peng
4. Jian Xu

In Radiology, Nuclear Medicine and Imaging

1. Biswajeet Pradhan
2. Dinggang Shen
3. Qian Wang
4. Deng-Ping Fan
5. Shanghua Gao
6. Ming-Ming Cheng

Citations in the corpus

• Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, methodology): “Covid-ct-dataset [27] is a CT dataset of COVID-19, which is mainly composed of CT images extracted from PDF files of COVID-19 papers in medRxiv and bioRxiv.”
• Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, methodology): “In addition, we collected the third party dataset [29], [30] from different sources to validate the federated learning methods.”
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “The COVID-CT dataset [81] includes 288 CT slices for COVID-19 confirmed cases thus far.”
• AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system (2020, background): “Zhao et al. [15] build a COVID-CT dataset which includes 288 CT slices of confirmed COVID-19 patients, collected from about 700 COVID-19 related publications on medRxiv and bioRxiv.”
• COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, methodology): “CT scans for COVID-19 and non COVID-19 were obtained from the publicly accessible COVID-CT Dataset [66].”
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, methodology): “The COVID-CT dataset from [5] has 746 CT scan images, with 349 images from COVID-19 patients and 397 from non-COVID-19 cases.”
• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, methodology): “[2] 2022 Collaboratively training between healthcare devices for creating a robust model without raw data exchange Cross-device Decentralized Simulation 3D CNN Custom FedAvg (RingAVG) Synchronous Asynchronous Random COVID-19 CT [169] Eye Disease [170] Skin Cancer [171] [21] 2022 Ring topology for DG”
• Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks (2020, methodology): “[36] is utilized to train and test the proposed model.”
• Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays (2020, background): “The authors of [8] established a publicly available collection of 275 CT scans”
• Survey of Explainable AI Techniques in Healthcare (2023, methodology): “xDNN was evaluated using multiple datasets, including the COVID-CT dataset [10] and the SARS-CoV-2 CT scan dataset [76].”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, neutral): “COVID-CT-Dataset [12] CT image 288 / 1000 Diagnosis”
• Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, methodology): “Multi-tasking learning approach [35] 349 COVID positive CT samples and 463 non-COVID-19 CT samples For binary classification with the JCS COVID-seg combination dataset, an accuracy of 83%, F1 score of 85%, and AUC95%, is obtained.”