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

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arxiv 2003.13865 v3 pith:L2X47SKO submitted 2020-03-30 cs.LG cs.CVeess.IVstat.ML

Xingyi Yang , Xuehai He , Jinyu Zhao , Yichen Zhang , Shanghang Zhang , Pengtao Xie This is my paper · ORCID
classification cs.LGcs.CVeess.IVstat.ML
keywords covid-19datasetdiagnosispatientsavailablecovid-ctdiagnosinglearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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 https://github.com/UCSD-AI4H/COVID-CT

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example

    cs.SD 2025-08 conditional novelty 5.0 of 10

    Adding ACGAN-synthesized cough spectrograms to the Coughvid training set moved a CNN classifier's single-split accuracy from 72% to 75%.

  2. UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography

    eess.IV 2025-07 reject novelty 5.0 of 10

    UGPL uses evidential uncertainty maps to select CT image patches for focused re-analysis and reports gains over baselines on kidney, lung, and COVID classification.

  3. Stable Vision Concept Transformers for Medical Diagnosis

    cs.CV 2025-06 reject novelty 4.0 of 10

    A vision transformer with a concept bottleneck and denoised diffusion smoothing is claimed to give stable concept explanations under input perturbations while keeping diagnostic accuracy.

  4. An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis

    cs.LG 2025-07 reject novelty 3.0 of 10

    A federated few-shot learning framework that adds differential privacy noise to Meta-SGD gradient updates achieves reasonable respiratory disease diagnosis accuracy without sharing patient images.

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