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基于深度学习的多模式瘤细分方法:一个叙事审查

Hengzhi Xue1, Yudong Yao2,3, Yueyang Teng1,4

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Quantitative imaging in medicine and surgery
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 方法通过有效地融合来自各种成像来源的数据,显著提高了多模式瘤细分的准确性. 本综述探讨了最近的DL进展,以改善临床诊断和治疗计划.

关键词:
多模态图像多模态图像融合的方法 融合的方法审查 审查 审查 审查 审查瘤细分 瘤细分

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科学领域:

  • 医疗图像处理 医学图像处理
  • 人工智能在医学中的应用
  • 放射学 放射学是一门学科.

背景情况:

  • 自动瘤细分对于临床诊断和治疗至关重要.
  • 多模态成像提供了比单模态方法更全面的瘤理解.
  • 深度学习 (DL) 方法在医学图像分析中表现出色.

研究的目的:

  • 提供最近基于深度学习的多模式瘤细分方法的全面概述.
  • 总结该领域的关键技术和发现.
  • 确定未来的研究方向.

主要方法:

  • 在PubMed和谷歌学者数据库的系统文献搜索 (2018年1月 - 2023年6月).
  • 使用的关键词:"多模式"",深度学习"",瘤细分".
  • 78篇英语文章的综述.

主要成果:

  • 引入公共数据集,评估指标和多模式数据处理技术.
  • 用于瘤细分的常见DL网络架构,策略和融合方法的摘要.
  • 分析不同的瘤细分任务.

结论:

  • 深度学习是一种用于多模式瘤细分的强大技术.
  • 融合方法使DL框架能够利用各种数据特征,提高细分的准确性.
  • 该研究强调了DL在推进多模式瘤细分方面的潜力.