论文标题
医学图像分析中的可解释深度学习模型
Explainable deep learning models in medical image analysis
论文作者
论文摘要
深度学习方法对于各种医学诊断任务非常有效，甚至击败了其中一些人的专家。但是，该算法的黑盒性质有限于临床使用。最近的解释性研究旨在表明影响模型最大的决策的特征。该领域的大多数文献评论都集中在分类学，道德和对解释的需求上。此处介绍了对当前可解释的深度学习对不同医学成像任务的应用的综述。从深度学习研究人员为临床最终用户设计系统的实际角度，这里讨论了各种方法，临床部署的挑战以及需要进一步研究的领域。
Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.
