在病中基于成像的深度学习:最近的进展和未来的前景
Meng Zhang1,2,3, Zheng Ye1, Enyu Yuan1
1Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
Insights into imaging
|February 15, 2024
概括
使用医学成像辅助的深度学习用于诊断和管理脏疾病. 这项技术为瘤和非瘤脏疾病提供了精确的支持.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 脏疾病包括瘤和非瘤状况.
- 医学成像分析对于疾病诊断和管理至关重要.
- 深度学习为医学成像提供先进的数据挖掘功能.
研究的目的:
- 审查基于成像的深度学习的方法.
- 探索脏疾病的最新临床应用.
- 讨论这项技术的挑战和未来前景.
主要方法:
- 关于基于成像的深度学习在病学当前文献的综述.
- 分析器官细分,病变检测,诊断,手术规划和预后方面的应用.
- 讨论与数据相关的挑战和伦理考虑.
主要成果:
- 基于成像的深度学习越来越多地应用于瘤和非瘤脏疾病.
- 该技术提高了脏疾病的划界,诊断和评估的准确性.
- 关键的挑战包括数据平衡,异质性,大小,可解释性和偏见评估.
结论:
- 基于成像的深度学习显示了支持病的诊断和管理的巨大潜力.
- 解决数据挑战和道德问题对于未来的发展至关重要.
- 本综述旨在指导泌尿科医生,脏科医生和放射科医生利用这项技术.
相关概念视频
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