对质瘤细分,局限性和未来前景的深度学习方法的审查
Cecilia Diana-Albelda1, Álvaro García-Martín1, Jesus Bescos1
1Video Processing and Understanding Lab, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Journal of imaging
|August 27, 2025
概括
本综述分析了深度学习 (DL) 模型,用于从MRI扫描中对脑瘤 (质瘤) 进行细分. 它评估了性能,效率和临床适用性,以指导未来的研究,以获得更好的患者结果.
科学领域:
- 医学成像分析
- 医学中的人工智能
- 神经瘤学
背景情况:
- 通过MRI精确分离质瘤对于临床管理至关重要.
- 瘤的复杂性带来了重要的细分挑战.
- 现有的深度学习方法需要对临床部署进行评估.
研究的目的:
- 对质瘤细分的深度学习 (DL) 模型进行全面审查.
- 弥合研究成果与临床应用之间的差距.
- 根据准确性,计算效率和临床适用性来评估模型.
主要方法:
- 在2025年之前对80多种最先进的DL模型 (CNN,变压器,混合) 进行系统审查.
- 在 BraTS 数据集的基准性能评估.
- 考虑强度,效率和瘤划分的适用性分析.
主要成果:
- 将模型分为基于CNN的纯变压器和混合架构.
- 细分精度和计算效率的比较.
- 确定技术性能和临床可用性之间的权衡.
结论:
- 深度学习显示了自动化质瘤细分的前景.
- 需要进一步的研究来优化现实世界临床环境的模型.
- 提高诊断结果的关键是平衡性能与效率.
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