用X光片预测新冠病情严重程度，模型准确率达80%。
Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures
- 融合三数据源构建大规模新冠严重度数据集，对比CNN与ViT表现。
- DenseNet161在三分类任务中准确率达80%，严重病例识别率70%。
- ViT在严重度回归任务中误差仅0.5676，适合临床辅助评估。
新冠疫情冲击医疗资源，促使机器学习助力诊断以减轻医生负担。胸部X光（CXRs）用于新冠诊断，但极少研究从X光预测病情严重程度。本研究整合三个数据源构建大规模新冠严重度数据集，评估ImageNet和CXRs预训练模型及视觉变换器（ViTs）在严重度回归与分类任务中的表现。预训练DenseNet161在三类严重度预测中总体准确率达80%，对轻、中、重症的准确率分别为77.3%、83.9%和70%。ViT在回归任务中表现最佳，平均绝对误差为0.5676，接近放射科医生预测评分。项目代码已公开。
原文摘要 · Abstract (English)
The COVID-19 pandemic strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribute to diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few studies predict the severity of a patient's condition from CXRs. In this study, we produce a large COVID severity dataset by merging three sources and investigate the efficacy of transfer learning using ImageNet- and CXR-pretrained models and vision transformers (ViTs) in both severity regression and classification tasks. A pretrained DenseNet161 model performed the best on the three class severity prediction problem, reaching 80% accuracy overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases, respectively. The ViT had the best regression results, with a mean absolute error of 0.5676 compared to radiologist-predicted severity scores. The project's source code is publicly available.
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