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).

翻译：可解释深度学习在人工智能领域获得了显著关注，尤其是在医学影像等需要准确且可解释的机器学习模型以实现有效诊断和治疗规划的领域。Grad-CAM是一种基线方法，能够突出显示深度学习模型决策过程中使用的图像中最关键的区域，从而增强结果的可解释性和可信度。它被广泛应用于分类和解释等计算机视觉任务中。本研究探讨了可解释深度学习的原理及其在医学影像中的相关性，讨论了各种可解释性技术及其局限性，并考察了Grad-CAM在医学影像中的应用。研究结果突出显示了可解释深度学习和Grad-CAM在提升医学影像深度学习模型准确性和可解释性方面的潜力。代码将在（待提供）处公开。