梯度加权类激活映射 (Grad-CAM):一个系统的文献综述
Abdul Muiz Fayyaz1, Said Jadid Abdulkadir2, Noureen Talpur2
1Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia.
Computers in biology and medicine
|October 18, 2025
概括
本研究回顾了梯度加权类激活映射 (Grad-CAM),这是一个关键的可解释人工智能 (XAI) 方法. 它强调了Grad-CAM的演变和应用,特别是在医疗成像中,以提高模型的可解释性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 可解释的人工智能 (XAI) 对机器学习 (ML) 和深度学习 (DL) 模型的信任至关重要.
- 梯度加权类激活映射 (Grad-CAM) 是解释卷积神经网络 (CNN) 的一个著名的XAI技术.
- 格拉德-CAM可视识别图像区域对于CNN决策至关重要.
研究的目的:
- 进行对梯度加权类激活映射 (Grad-CAM) 的系统文献审查 (SLR).
- 分析Grad-CAM的进步,特别是在医学成像领域.
- 在ML和DL框架内探索Grad-CAM应用.
主要方法:
- 在主要的学术数据库 (Scopus,科学网,IEEE Xplore,ScienceDirect) 进行系统的文献搜索.
- 从 427 篇已识别的文章中对 51 篇经过同行评审的出版物进行了深入审查.
- 分析重点关注的是2020-2024年期间.
主要成果:
- 关于Grad-CAM的演变和当前研究趋势的全面概述.
- 识别各种Grad-CAM技术及其与各种ML/DL架构的集成.
- 详细了解Grad-CAM优化策略及其对模型可解释性的影响.
结论:
- Grad-CAM是提高CNN透明度的一个重要工具,尤其是在医学成像方面.
- 该审查为了解Grad-CAM的能力和未来方向提供了宝贵的资源.
- 进一步的研究可以利用Grad-CAM来提高诊断准确性和医疗保健中可靠的AI.
相关概念视频
01:19Gradient Vectors and Their Applications
Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...
01:27Significance of the Gradient Vector
A surface defined by a function of two variables can be understood by examining how it changes along specific directions. When one variable is held constant, the surface reduces to a curve that reflects variation in the other variable. For example, fixing one variable and moving parallel to a coordinate axis produces a cross-sectional curve. The slope of this curve at a given point represents how the function changes in that particular direction, providing a measure of local steepness.By...
01:27Gradient Fields
A gradient field is a vector field derived from a scalar field. A scalar field assigns a single numerical value to every point in space, such as temperature, pressure, or electric potential. The gradient field describes how that value changes from point to point. It gives both the direction of the fastest increase and the rate of change in that direction.For a scalar field f(x, y), the gradient is written as\begin{equation*}\nabla f=\left\langle \jfrac{\partial f}{\partial x},\jfrac{\partial...