# Is Grad-CAM Explainable in Medical Images?

arXiv: https://arxiv.org/abs/2307.10506 · HTML: https://mlsanity.com/paper/2307.10506/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)

## Built on by (most cited first, 10 of 10)

- [CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning](https://mlsanity.com/paper/2503.05604/) (2025)
- [Ordinal-Aware Calibration for Ordinal Classification](https://mlsanity.com/paper/2410.15658/) (2024)
- [Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation](https://mlsanity.com/paper/2407.08546/) (2024)
- [EU-Nets: Enhanced, Explainable and Parsimonious U-Nets](https://mlsanity.com/paper/2502.18122/) (2025)
- [Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability](https://mlsanity.com/paper/2504.05483/) (2025)
- [An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images](https://mlsanity.com/paper/2509.08780/) (2025)
- [Analysis of Invasive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptation](https://mlsanity.com/paper/2512.00129/) (2025)
- [Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models](https://mlsanity.com/paper/2602.20511/) (2026)
- [Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification](https://mlsanity.com/paper/2604.08502/) (2026)
- [Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection](https://mlsanity.com/paper/2609.06699/) (2026)

## Abstract (arXiv)

Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model's decision-making process, increasing interpretability and trust in the results. It is applied in many computer vision (CV) tasks such as classification and explanation. This study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (will be available).