mlsanityData to 14 Oct 2026

Is Grad-CAM Explainable in Medical Images?

arXiv 2307.10506 ↗First posted 20 Jul 2023eess.IV cs.CV cs.CYcited by 10

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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).

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

6 of 22 references.

References · 6

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  4. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  5. 2018Rotation Equivariant CNNs for Digital Pathologycited by 422
  6. 2017Deep Learning for Medical Image Analysiscited by 401

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.

  1. 2025CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learningcited by 6
  2. 2024Ordinal-Aware Calibration for Ordinal Classificationcited by 3
  3. 2024Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentationcited by 0
  4. 2025EU-Nets: Enhanced, Explainable and Parsimonious U-Netscited by 0
  5. 2025Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretabilitycited by 0
  6. 2025An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Imagescited by 0
  7. 2025Analysis of Invasive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptationcited by 0
  8. 2026Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Modelscited by 0
  9. 2026Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classificationcited by 0
  10. 2026Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detectioncited by 0