Is Grad-CAM Explainable in Medical Images?
It addresses the need for interpretable AI models in medical imaging for diagnosis and treatment planning, but is incremental as it reviews existing techniques without introducing new methods.
This study explores the principles and applications of Explainable Deep Learning, particularly Grad-CAM, in medical imaging to improve model accuracy and interpretability, but does not report specific numerical results.
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).