游戏化众包肺部超声数据集
哈佛医学院创建，含2384个肺部超声视频片段，经专家标注用于B线分类，通过游戏化众包提高标签质量。
基本信息
资源简介
该数据集由哈佛医学院研究团队创建，包含203名患者的2384个肺部超声视频片段，经六位专家标注为无B线、离散B线或融合B线，用于通过游戏化众包平台提升标签质量，支持机器学习模型在肺部超声B线分类中的应用，旨在提高诊断速度和准确性。
下载信息
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使用方式
数据集说明
游戏化众包肺部超声数据集 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/2306.06773v1
- 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:Study Objective: Machine learning models have advanced medical image processing and can yield faster, more accurate diagnoses. Despite a wealth of available medical imaging data, high-quality labeled data for model training is lacking. We investigated whether a gamified crowdsourcing platform enhanced with inbuilt quality control metrics can produce lung ultrasound clip labels comparable to those from clinical experts.
Methods: 2,384 lung ultrasound clips were retrospectively collected from 203 patients. Six lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create two sets of reference standard labels (195 training set clips and 198 test set clips). Sets were respectively used to A) train users on a gamified crowdsourcing platform, and B) compare concordance of the resulting crowd labels to the concordance of individual experts to reference standards.
Results: 99,238 crowdsourced opinions on 2,384 lung ultrasound clips were collected from 426 unique users over 8 days. On the 198 test set clips, mean labeling concordance of individual experts relative to the reference standard was 85.0% +/- 2.0 (SEM), compared to 87.9% crowdsourced label concordance (p=0.15). When individual experts' opinions were compared to reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs. 80.8% +/- 1.6; p<0.001).
Conclusion: Crowdsourced labels for B-line classification vi
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