Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify four core elements of interpretability: localization, visual recognizability, physical attribution, and transparency. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research.

医疗图像中机器学习模型的可解释性是一项重要的研究方向，本文通过对现实任务和目标进行推理，提出了可解释性的四个核心要素：定位，视觉识别能力，物理归属和透明度。通过形式化医疗图像领域的可解释性需求和具体目标，本研究为模型设计者和实践者提供了实用的教学资料，迫使医疗图像领域的模型开发者更深入地思考可解释性的实现，并提出了可解释性研究的未来方向。

医学影像机器学习可解释性框架