论文标题
生物医学信号和图像处理中的可解释AI（XAI）：承诺和挑战
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
论文作者
论文摘要
人工智能已在学科和领域中普遍存在，生物医学图像和信号处理也不例外。对该主题的增长和广泛的兴趣引发了一项巨大的研究活动，这反映在指数的研究工作中。通过研究大规模和多样化的生物医学数据，机器和深度学习模型彻底改变了各种任务，例如建模，分割，注册，分类和综合，胜过传统技术。但是，将结果转化为生物学/临床上可解释的信息的困难在于阻止其在现场的全部剥削。可解释的AI（XAI）试图通过提供使模型可解释并提供解释的方法来填补这一翻译差距。到目前为止，已经提出了不同的解决方案，并且正在从社区中获得越来越多的兴趣。本文旨在在生物医学数据处理中提供有关XAI的概述，并指出即将在2022年3月出现的IEEE Signal Processing杂志的生物医学图像和信号处理中深入学习的特刊。
Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.
