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.

翻译：近期公开可获取的大规模医学影像数据集激增，催生了大量用于心血管图像分类与分析的人工智能模型。与此同时，这些模型可能产生的重大影响推动了可解释人工智能（XAI）方法的发展，旨在解释模型在特定图像输入下的预测结果。然而，许多方法并非基于领域专家视角开发或评估，其解释也未结合医学专业知识或领域知识进行语境化。本文提出一种新型框架及Python工具库MiMICRI，可为心血管图像分类模型提供以领域为中心的反事实解释。MiMICRI支持用户交互式选择并替换医学图像中对应形态结构的区域，通过生成的反事实样本评估各区域对模型预测的影响，并基于已知医学事实验证模型性能。我们联合两位医学专家对该工具库进行评估，结果表明：以领域为中心的XAI方法能增强模型解释的可理解性，并帮助专家结合相关领域知识对模型进行推理。但研究也揭示出生成的反事实样本在临床合理性方面存在隐忧。最后，我们探讨了MiMICRI框架的泛化性与可信度，并总结了本研究发现对医疗场景中面向领域中心的XAI方法发展的启示。