在医学图像分析中,你只需要注意吗? 一个审查 一个审查
IEEE journal of biomedical and health informatics
|December 29, 2023
概括
结合卷积神经网络 (CNN) 和变压器的混合模型通过整合本地和全球数据特征来增强医疗图像分析. 这篇评论探讨了这些CNN-Transf/Attention架构,以改善医疗保健中的概括性.
科学领域:
- 人工智能在医学中的应用
- 对于医学成像的深度学习
背景情况:
- 医学成像数据占医疗保健信息的近90%,对诊断和治疗至关重要.
- 卷积神经网络 (CNN) 在医学图像分析 (MIA) 中擅长局部特征提取,但在全球背景下扎.
- 变压器提供全球上下文建模,但需要大量的数据和计算资源.
结论:
- 混合CNN-Transf/Attention模型代表了医学图像分析的重大进步.
- 需要进一步的研究才能充分利用它们的概括和临床影响的潜力.
- 拟议的框架可以指导开发新的领域概括和适应技术.
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