368 COVID-19 chest CT studies and 3,680 axial slices, each paired with the original free-text radiology report written in Chinese by the reporting radiologist.
Each row is one CT examination: ten axial lung-window slices, plus the radiologist's findings and conclusion exactly as written, plus sex, age and the RT-PCR result. The reports are not translated and not reduced to labels — they are the raw clinical text, with incidental findings, negative statements about unrelated organs, hedging, and comparisons against the patient's previous scan.
The data was collected between 18 January and 8 March 2020 at the First Affiliated Hospital of Jinan University (Guangzhou) and the Fifth Affiliated Hospital of Sun Yat-sen University (Zhuhai), and was released with our TNNLS paper on Medical-VLBERT, a model that generates these reports from the images.
Both charts below count studies (examinations), not patients: because patients were re-scanned every few days, the 368 studies come from roughly 96 people.
Studies per ten-year band
Studies per week, weeks beginning Monday, 18 Jan – 8 Mar 2020
77% of the reports compare against an earlier scan of the same patient (对比2020-02-08CT检查, 较前吸收), and consecutive studies of one patient have near-duplicate reports. A random study-level split puts a patient on both sides of the train/test boundary and inflates report-generation scores.
Per-study patient identity was not preserved with the data and is no longer recoverable.
The release therefore ships patient_block — 67 (sex, age) cells
that are guaranteed to contain every study of a given patient — and a
recommended_split built from whole blocks, so no patient can straddle the
boundary. Use it, or group by patient_block yourself.
| Split | Studies | Blocks | Female | Male | Age range | Mean age |
|---|---|---|---|---|---|---|
| train | 296 | 43 | 160 | 136 | 3–75 | 46.8 |
| validation | 35 | 12 | 15 | 20 | 16–71 | 49.8 |
| test | 37 | 12 | 21 | 16 | 20–80 | 51.7 |
This split is for leakage-free benchmarking and is not the split used in the TNNLS paper, so results are not directly comparable to the published numbers. The paper also used 3 slices per study; this release provides all 10.
The dataset is hosted on the Hugging Face Hub. Access is gated: the dataset card is public, the files are released once you accept the data use terms. This replaces the old arrangement of emailing a signed confidentiality agreement.
from datasets import load_dataset
ds = load_dataset("guangyil/COVID-19-CT-Chinese", split="train")
ex = ds[0]
print(ex["case_uid"], ex["sex"], ex["age"], ex["rt_pcr"])
print(ex["findings_zh"]) # 检查所见
print(ex["conclusion_zh"]) # 诊断意见
ex["images"][0].show() # 10 axial slices, PIL images
# use the leakage-free split — do not split randomly
train = ds.filter(lambda x: x["recommended_split"] == "train")
test = ds.filter(lambda x: x["recommended_split"] == "test")
Released under CC BY-NC-SA 4.0 — attribution, non-commercial, share-alike. On top of the licence you agree not to re-identify anyone, not to redistribute the data (point people here instead), and not to use the data or any model trained on it for clinical decision-making. The full terms are on the dataset card.
The Chinese medical encyclopedia corpus used for the knowledge-pretraining stage of Medical-VLBERT is not redistributed: it was scraped from third-party medical websites and is not ours to relicense. It is not needed to use this dataset. The chest X-ray dataset CX-CHR, also used in the paper, is a third-party dataset and is likewise not distributed here.
If you use this dataset, please cite:
@article{liu2021medicalvlbert,
title = {Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT
Report Generation With Alternate Learning},
author = {Liu, Guangyi and Liao, Yinghong and Wang, Fuyu and Zhang, Bin and
Zhang, Lu and Liang, Xiaodan and Wan, Xiang and Li, Shaolin and
Li, Zhen and Zhang, Shuixing and Cui, Shuguang},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
volume = {32},
number = {9},
pages = {3786--3797},
year = {2021},
doi = {10.1109/TNNLS.2021.3099165}
}