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来自胸部X射线的肺结核放射性表现的弱定位:系统性审查
Degaga Wolde Feyisa1, Yehualashet Megersa Ayano1, Taye Girma Debelee1,2
1Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
使用胸部X射线 (CXRs) 检测肺结核 (PTB) 是一个挑战. 本综述探讨了弱监督的深度学习方法,用于在CXR上定位PTB表现,解决注释限制.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 肺结核 (PTB) 是全球传染病死亡的主要原因.
- 胸部放射 (CXR) 对于诊断PTB至关重要,但特征解释严重依赖于放射科医生的专业知识.
- 深度学习 (DL) 显示了在CXR中检测PTB的前景,但完全监督的方法需要广泛,昂贵和主观的注释.
研究的目的:
- 审查在胸部X射线中对PTB放射性表现的弱监督细分和定位方法.
- 为了识别用于结核病检测的常用公共CXR数据集.
- 讨论弱局部化技术在PTB诊断中的优点和挑战.
主要方法:
- 在CXR中检测PTB的弱监督学习方法的文献综述.
- 在结核病鉴定研究中使用的公开CXR数据集的分析.
- 使用有限的注释,讨论本地化PTB表现的技术.
主要成果:
- 弱监督的本地化可以识别图像区域对基于DL的PTB分类最有影响.
- 这些方法为医疗成像中广泛的数据注释所面临的挑战提供了潜在的解决方案.
- 该审查突出了突出PTB表现在CXR上的各种方法.
结论:
- 弱监管方法为CXR中PTB检测提供了完全监管细分的可行替代方案.
- 需要进一步的研究来解决这些技术的局限性和挑战.
- 推进这些方法可以提高PTB诊断的准确性和效率.
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