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Related Concept Videos

Gradient Vectors and Their Applications01:19

Gradient 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...
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Related Experiment Video

Updated: Jul 6, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

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Published on: January 7, 2019

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Grad-CAM (Gradient-weighted Class Activation Mapping): A systematic literature review.

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
PubMed
Summary

This study reviews Gradient-weighted Class Activation Mapping (Grad-CAM), a key Explainable Artificial Intelligence (XAI) method. It highlights Grad-CAM's evolution and applications, particularly in medical imaging for enhanced model interpretability.

Keywords:
Artificial IntelligenceDeep LearningExplainable Artificial IntelligenceGrad-CAMSystematic Literature Review

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Last Updated: Jul 6, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Computer Vision

Background:

  • Explainable Artificial Intelligence (XAI) is vital for trust in Machine Learning (ML) and Deep Learning (DL) models.
  • Gradient-weighted Class Activation Mapping (Grad-CAM) is a prominent XAI technique for interpreting Convolutional Neural Networks (CNNs).
  • Grad-CAM visually identifies image regions crucial for CNN decision-making.

Purpose of the Study:

  • To conduct a Systematic Literature Review (SLR) on Gradient-weighted Class Activation Mapping (Grad-CAM).
  • To analyze Grad-CAM advancements, particularly in medical imaging.
  • To explore Grad-CAM applications within ML and DL frameworks.

Main Methods:

  • Systematic literature search across major academic databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect).
  • In-depth examination of 51 selected peer-reviewed publications from a pool of 427 identified articles.
  • Analysis focused on the period 2020-2024.

Main Results:

  • Comprehensive overview of Grad-CAM's evolution and current research trends.
  • Identification of various Grad-CAM techniques and their integration with diverse ML/DL architectures.
  • Detailed insights into Grad-CAM optimization strategies and its impact on model interpretability.

Conclusions:

  • Grad-CAM is a significant tool for enhancing transparency in CNNs, especially in medical imaging.
  • The review provides a valuable resource for understanding Grad-CAM's capabilities and future directions.
  • Further research can leverage Grad-CAM for improved diagnostic accuracy and trustworthy AI in healthcare.