在胸部X射线图像中检测肺炎的深度学习:一项全面的调查
Raheel Siddiqi1, Sameena Javaid1
1Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan.
Journal of imaging
|August 28, 2024
概括
深度学习,特别是视觉转换器 (ViTs),在胸部X射线 (CXRs) 中显示出肺炎检测的希望. 需要进一步的研究来解决诸如数据集偏差和模型可解释性等局限性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 使用胸部X射线 (CXR) 检测肺炎对全球健康至关重要.
- 深度学习 (DL) 提供了自动化和辅助诊断的潜力.
- 随着COVID-19的爆发,人们越来越需要有效的诊断工具.
研究的目的:
- 在CXR中全面分析DL用于肺炎检测的应用.
- 在2020-2023年COVID-19期间评估DL方法.
- 确定当前DL方法的局限性和有效性.
主要方法:
- 对基于CXR的肺炎检测DL技术进行系统的文献审查和分析.
- 评估着重于2020-2023年时间框架的方法.
- 分析DL模型作为放射科医生的辅助或替代品.
主要成果:
- 视觉转换器 (ViT) 在CXR中显示出对肺炎检测有显著的前景.
- 目前的ViT方法面临的局限性包括有偏见的数据集和缺乏数据/代码可用性.
- 在模型可解释性,比较性,类不平衡性和对抗性攻击方面仍然存在挑战.
结论:
- ViT代表了CXR肺炎检测中最有前途的DL技术.
- 解决数据集的局限性,可解释性和稳定性对于ViT进步至关重要.
- 进一步的研究是克服所识别的挑战和提高临床效用必不可少的.
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