深度学习模型用于CT图像分类:综合文献综述
Isah Salim Ahmad1,2, Jingjing Dai1,2, Yaoqin Xie1,2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Quantitative imaging in medicine and surgery
|January 22, 2025
概括
深度学习 (DL) 显著增强了计算机断层扫描 (CT) 图像分析,用于检测COVID-19和肺结节等疾病. 先进的DL模型提高了诊断的准确性和效率,尽管在实施方面仍然存在挑战.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 放射学和瘤学 放射学和瘤学
背景情况:
- 计算机断层扫描 (CT) 对于诊断严重疾病,特别是呼吸系统疾病和癌症至关重要.
- 深度学习 (DL) 正在彻底改变CT图像分析,提高诊断准确性和效率.
- 本综述侧重于DL在COVID-19检测和肺结节分类中的应用,使用CT.
研究的目的:
- 审查先进的深度学习方法对CT成像分析的影响.
- 突出DL在COVID-19检测和肺结节分类中的应用.
- 探索DL架构在医学成像中的演变.
主要方法:
- 从2013年到2023年对CT图像分析中的DL进行了全面的文献搜索.
- 研究了从卷积神经网络 (CNNs) 到基础模型 (FMs) 的演变.
- 专注于同行评审的研究和来自主要数据库的评论文章.
主要成果:
- 深度学习,特别是基础模型,已经改变了CT图像分析,提高了诊断能力.
- 在COVID-19检测和肺癌查方面观察到重大进展.
- 技术挑战包括数据变化,数据集大小和计算需求,其中包括转移学习和数据增强等解决方案.
结论:
- 深度学习在推进COVID-19的CT分析和肺结节检测方面发挥着关键作用.
- 将DL模型集成到临床工作流程中,有望提高诊断准确性和效率.
- 持续的研究,合作和道德考虑对于DL的临床整合至关重要.
相关概念视频
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Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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