通过人工智能推进医疗决策:全面探索从卷积神经网络到囊网络的演变
Ichrak Khoulqi1, Zakariae El Ouazzani2
1DICC Team, Data4Earth Laboratory, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco.
Journal of imaging
|January 27, 2026
概括
本综述将卷积神经网络 (CNN) 和囊网络 (CapsNets) 进行医学图像分析. 囊网络提供了更好的可靠性和空间理解,补充了CNN,以提供更好的医疗决策支持.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 广泛用于医学诊断,但与空间可变性作斗争.
- 囊网络 (CapsNets) 通过编码空间关系,增强模型概括性和可靠性而出色.
研究的目的:
- 进行文献综述,比较CNN和CapsNets用于医学图像分析.
- 分析它们在医疗决策支持系统中的应用.
主要方法:
- 在主要科学数据库中对2018-2025年的研究进行系统审查.
- 专注于使用基准数据集的应用程序:BraTS,INbreast,ISIC和COVIDx.
主要成果:
- CNNs显示诊断能力,但对解剖学变异敏感.
- 由于空间编码,CapsNets表现出卓越的稳定性和通用性.
- 建筑原则,性能和可解释性的比较.
结论:
- CapsNets和CNN在医疗决策中发挥着互补的作用.
- 未来的方向包括用于临床环境的混合,可解释和高效的深度学习系统.
- 对早期疾病预防和改善患者存活率的潜力.
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