After releasing this dataset, we received several feedbacks expressing concerns about the usability of this dataset. The major concerns are summarized as follows. First, when the original CT images are put into papers, the quality of these images is degraded, which may render the diagnosis decisions less accurate. The quality degradation includes: the Hounsfield unit (HU) values are lost; the number of bits per pixel is reduced; the resolution of images is reduced. Second, the original CT scan contains a sequence of CT slices, but when put into papers, only a few key slices are selected, which may have negative impact on diagnosis as well.
We consulted the aforementioned radiologist at Tongji Hospital regarding these two concerns. According to the radiologist, the issues raised in these concerns do not significantly affect the accuracy of diagnosis decision-making. First, experienced radiologists are able to make an accurate diagnosis from low quality CT images. For example, given a photo taken by smart phone of the original CT image, experienced radiologists can make an accurate diagnosis by just looking at the photo, though the CT image in the photo has much lower quality than the original CT image. Likewise, the quality gap between CT images in papers and original CT images will not largely hurt the accuracy of diagnosis. Second, while it is preferable to read a sequence of CT slices, oftentimes a single-slice of CT contains enough clinical information for accurate decision-making.
The COVID-CT-Dataset has 349 CT images containing clinical findings of COVID-19 from 216 patients. They are in ./Images-processed/CT_COVID.zip
Non-COVID CT scans are in ./Images-processed/CT_NonCOVID.zip
We provide a data split in ./Data-split
.Data split information see README for DenseNet_predict.md
The meta information (e.g., patient ID, patient information, DOI, image caption) is in COVID-CT-MetaInfo.xlsx
The images are collected from COVID19-related papers from medRxiv, bioRxiv, NEJM, JAMA, Lancet, etc. CTs containing COVID-19 abnormalities are selected by reading the figure captions in the papers. All copyrights of the data belong to the authors and publishers of these papers.
The dataset details are described in this preprint: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
If you find this dataset and code useful, please cite:
@article{zhao2020COVID-CT-Dataset,
title={COVID-CT-Dataset: a CT scan dataset about COVID-19},
author={Zhao, Jinyu and Zhang, Yichen and He, Xuehai and Xie, Pengtao},
journal={arXiv preprint arXiv:2003.13865},
year={2020}
}
We developed two baseline methods for the community to benchmark with.The code are in the "baseline methods" folder and the details are in the readme files under that folder. The methods are described in Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
If you find the code useful, please cite:
@Article{he2020sample,
author = {He, Xuehai and Yang, Xingyi and Zhang, Shanghang, and Zhao, Jinyu and Zhang, Yichen and Xing, Eric, and Xie, Pengtao},
title = {Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans},
journal = {medrxiv},
year = {2020},
}
COVID-CT-MetaInfo.xlsx
.COVID-CT-MetaInfo.xlsx
, images with the form of 2020.mm.dd.xxxx
are crawled from bioRxiv or medRxiv. The DOIs for these preprints are 10.1101/2020.mm.dd.xxxx
.亚马逊网络服务(AWS)已形成一个公共AWS COVID-19数据湖 ,这是与新型冠状病毒的传播及相关疾病有关的集中数据集。 AWS在4月8日表示,它正在与合作伙伴合作,免费提供不断增长的COVID-19数据集,并使其保持最新。 AWS已利用Johns Hopkins和《纽约时报》的COVID-19病例跟踪数据,Definitive Healthcare的病床可用性以及艾伦AI研究所的45,000
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�� Note: please do not claim diagnostic performance of a model without a clinical study! This is not a kaggle competition dataset. Please read this paper about evaluation issues: https://arxiv.org/abs