论文标题
Sidu：可解释AI的相似性差异和唯一方法
SIDU: Similarity Difference and Uniqueness Method for Explainable AI
论文作者
论文摘要
技术人工智能的新品牌（可解释的AI）研究重点是试图打开“黑匣子”并提供一些解释性。本文介绍了一种新颖的视觉解释方法，该方法以显着图的形式进行深度学习网络，该方法可以有效地定位整个对象区域。与当前的最新方法相反，提出的方法显示出非常有希望的视觉解释，可以获得对人类专家的更大信任。定量和定性评估都是对一般和临床数据集进行的，以确认所提出方法的有效性。
A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.
