医疗保健领域的 XAI 模型联合学习:帕金森病案例研究

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pietro Ducange, Francesco Marcelloni, Alessandro Renda, Fabrizio Ruffini
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引用次数: 0

摘要

人工智能(AI)系统越来越多地应用于医疗保健领域,但要使其完全值得信赖并符合现代法规和社会需求,还有一些挑战尚未完全克服。首先,对训练人工智能系统至关重要的敏感健康数据通常存储和管理在多个独立的医疗中心,由于隐私限制而无法共享,从而阻碍了在学习模型中使用所有可用信息。此外,此类系统的透明度和可解释性正变得越来越紧迫,尤其是在 "不透明 "或 "黑盒 "模型被普遍使用的时候。最近,人们开始研究应对这些挑战的技术和算法解决方案:一方面,联合学习(FL)已被提出作为多方协作模型训练的范例,而无需披露任何私人原始数据;另一方面,可解释人工智能(XAI)研究旨在通过可解释的设计方法或事后解释技术,提高人工智能系统的可解释性。在本文中,我们将重点放在医疗案例研究上,即预测帕金森病的进展,并假设原始数据来自不同的医疗中心,且由于隐私限制,无法收集数据进行集中训练。我们的目标是研究 XAI 模型的 FL 如何能够实现良好的准确性和可信度。我们的分析采用了认知和生物启发方法:可解释的基于设计的模糊规则系统的 FL 和使用联合版本的 SHAP 事后解释技术解释的神经网络的 FL。我们对这两种方法的准确性、可解释性和可解释性进行了分析,并在几种数据分布情况下改变了异质性的程度。虽然神经网络通常更准确,但结果表明,基于模糊规则的系统在联合环境中实现了具有竞争力的性能,并在可解释性和透明度方面呈现出理想的特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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