Grad-CAM 在医学图像中是否具有可解释性？
Is Grad-CAM Explainable in Medical Images?
- XIM University（克西姆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述可解释深度学习及 Grad-CAM 在医学成像中的原理、技术局限与应用，表明其有助于提升模型诊断的准确性和可解释性。
AI中文摘要：
可解释深度学习已在人工智能（AI）领域获得显著关注，尤其是在医学成像等领域：在这些领域中，准确且可解释的机器学习模型对于有效的诊断和治疗规划至关重要。Grad-CAM 是一种基线方法，可突出深度学习模型决策过程中使用的图像最关键区域，从而提高结果的可解释性和可信度。它被应用于分类和解释等许多计算机视觉（CV）任务。本研究探讨了可解释深度学习的原理及其与医学成像的相关性，讨论了多种可解释性技术及其局限性，并考察了 Grad-CAM 在医学成像中的应用。研究结果强调了可解释深度学习和 Grad-CAM 在提升医学成像中深度学习模型的准确性与可解释性方面的潜力。代码可在（将会提供）处获取。
英文摘要：
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).
