Typical state of the art flow cytometry data samples consists of measures of more than 100.000 cells in 10 or more features. AI systems are able to diagnose such data with almost the same accuracy as human experts. However, there is one central challenge in such systems: their decisions have far-reaching consequences for the health and life of people, and therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI method, called ALPODS, which is able to classify (diagnose) cases based on clusters, i.e., subpopulations, in the high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable for human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison to a selection of state of the art explainable AI systems shows that ALPODS operates efficiently on known benchmark data and also on everyday routine case data.
翻译:光学数据系统能够以与人类专家几乎相同的准确性来诊断这些数据。然而,在这种系统中有一个中心挑战:它们的决定对人的健康和生活具有深远的影响,因此,光学数据系统的决定需要人类能够理解和合理理解。在这项工作中,我们提出了一个新型的可解释的光学方法,称为ALPODS,它能够根据群集,即高维数据中的亚群,对(诊断)案例进行分类。ALPODS能够以人类专家可以理解的形式解释其决定。对于已查明的亚群,产生了以典型的域专家语言表达的模糊推理规则。基于这些规则的直观化方法,使人类专家能够理解光学系统所使用的推理。与可解释的光学系统状态的比较表明,光学数据以已知的基准数据和日常例行案例数据为主。