Abstract
The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classification and analysis. At the same time, the potentially significant impacts of these models have motivated the development of a range of explainable AI (XAI) methods that aim to explain model predictions given certain image inputs. However, many of these methods are not developed or evaluated with domain experts, and explanations are not contextualized in terms of medical expertise or domain knowledge. In this paper, we propose a novel framework and python library, MiMICRI, that provides domain-centered counterfactual explanations of cardiovascular image classification models. MiMICRI helps users interactively select and replace segments of medical images that correspond to morphological structures. From the counterfactuals generated, users can then assess the influence of each segment on model predictions, and validate the model against known medical facts. We evaluate this library with two medical experts. Our evaluation demonstrates that a domain-centered XAI approach can enhance the interpretability of model explanations, and help experts reason about models in terms of relevant domain knowledge. However, concerns were also surfaced about the clinical plausibility of the counterfactuals generated. We conclude with a discussion on the generalizability and trustworthiness of the MiMICRI framework, as well as the implications of our findings on the development of domain-centered XAI methods for model interpretability in healthcare contexts.
Abstract (translated)
近年来，公开可获取的大型医疗影像数据集的普及导致了许多心血管图像分类和分析的人工智能（AI）模型的出现。与此同时，这些模型的潜在影响也促使开发了一系列可解释AI（XAI）方法，旨在解释给定图像输入的模型预测。然而，许多这些方法都没有经过领域专家的开发或评估，并且解释没有针对医疗专业知识或领域知识进行contextual化。在本文中，我们提出了一个新颖的框架和Python库，MiMICRI，为心血管图像分类模型的领域中心反事实解释提供支持。MiMICRI使用户可以交互式选择和替换医学图像中与形态结构对应的区域。从反事实产生的结果中，用户可以 then评估每个片段对模型预测的影响，并验证模型是否符合已知医疗事实。我们对这个库进行了两个医疗专家的评估。我们的评估表明，以领域为中心的XAI方法可以增强模型解释的可解释性，并帮助专家在相关领域知识的基础上对模型进行推理。然而，也担忧反事实产生的临床可解释性。我们得出结论，MiMICRI框架的可解释性和可靠性，以及我们的研究结果对 healthcare 环境中模型可解释性发展的影响，都存在一定的意义。
URL
https://arxiv.org/abs/2404.16174
