COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning
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Abstract (arXiv)
Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 1 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and subjects infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2020.
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Citing papers per year: 2021: 7; 2022: 3.
10 papers.
- 2021Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Datacited by 10
- 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
- 2021COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachcited by 8
- 2021Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learningcited by 8
- 2021Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Networkcited by 6
- 2022Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomographycited by 3
- 2021Online unsupervised Learning for domain shift in COVID-19 CT scan datasetscited by 2
- 2021Data Shapley Value for Handling Noisy Labels: An application in Screening COVID-19 Pneumonia from Chest CT Scanscited by 1
- 2022COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Imagescited by 1
- 2021COVID-19 Detection in Computed Tomography Images with 2D and 3D Approachescited by 0
- 2022Recent Progress in Transformer-based Medical Image Analysis13 Aug 2022
- 2022COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images7 Jun 2022
- 2022Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography7 Mar 2022
- 2021Data Shapley Value for Handling Noisy Labels: An application in Screening COVID-19 Pneumonia from Chest CT Scans17 Oct 2021
- 2021Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data20 Aug 2021
- 2021Online unsupervised Learning for domain shift in COVID-19 CT scan datasets31 Jul 2021
- 2021Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network31 May 2021
- 2021COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches16 May 2021
- 2021Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning11 Apr 2021
- 2021COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach11 Feb 2021