SPGC-COVID
SPGC-COVID数据集包含130例胸部CT扫描，用于区分COVID-19、社区获得性肺炎和正常病例，涵盖多中心、多成像设置，并包含心血管疾病或手术史患者数据。
基本信息
资源简介
SPGC-COVID数据集是由康考迪亚大学信息系统工程研究所开发的，用于区分COVID-19、社区获得性肺炎（CAP）和正常病例的胸部CT扫描数据集。该数据集包含130个案例，涵盖了多种成像设置和不同的医疗中心。数据集的创建旨在通过深度学习框架提高对COVID-19的诊断准确性，并解决训练和测试数据集之间特征差异的问题。此外，数据集还包括了具有心血管疾病或手术史的患者的CT扫描，增加了数据集的复杂性和实用性。
下载信息
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使用方式
数据集说明
SPGC-COVID 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2109.09241v3
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers using various protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. We adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset
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