Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Compared to “black box” algorithms such as deep learning, the responses of CBR systems can be justified easily using the similar cases as examples. However, this possibility is often under-exploited and the explanations provided by most CBR systems are limited to the display of the similar cases.

In this paper, we propose a CBR method that can be both executed automatically as an algorithm and presented visually in a user interface for providing visual explanations or for visual reasoning. After retrieving similar cases, a visual interface displays quantitative and qualitative similarities between the query and the similar cases, so as one can easily classify the query through visual reasoning, in a fully explainable manner. It combines a quantitative approach (visualized by a scatter plot based on Multidimensional Scaling in polar coordinates, preserving distances involving the query) and a qualitative approach (set visualization using rainbow boxes). We applied this method to breast cancer management. We showed on three public datasets that our qualitative method has a classification accuracy comparable to k-Nearest Neighbors algorithms, but is better explainable. We also tested the proposed interface during a small user study. Finally, we apply the proposed approach to a real dataset in breast cancer. Medical experts found the visual approach interesting as it explains why cases are similar through the visualization of shared patient characteristics.

基于案例的推理（CBR）是类比推理的一种形式，其中（新）查询案例的解决方案是使用先前已知案例及其解决方案的数据库来确定的。从数据库中检索与查询相似的案例，然后将其解决方案应用于查询。在医学上，病例通常与患者相对应，问题在于将患者分类为诊断或治疗类别。与诸如深度学习之类的“黑匣子”算法相比，使用类似的案例作为示例，可以轻松地证明CBR系统的响应是正确的。但是，这种可能性常常未被充分利用，大多数CBR系统提供的解释仅限于类似情况的展示。

在本文中，我们提出了一种CBR方法，该方法既可以作为算法自动执行，又可以在用户界面中以视觉方式呈现，以提供视觉解释或视觉推理。检索相似案例后，可视界面显示查询与相似案例之间的定量和定性相似性，以便人们可以通过视觉推理轻松地对查询进行分类，并且完全可以解释。它结合了定量方法（通过基于极坐标中的多维缩放的散点图进行可视化，保留涉及查询的距离）和定性方法（使用彩虹框设置可视化）。我们将此方法应用于乳腺癌治疗。我们在三个公共数据集上表明，我们的定性方法具有与k相当的分类精度-最近的邻居算法，但可以更好地解释。我们还在小型用户研究中测试了建议的界面。最后，我们将提出的方法应用于乳腺癌的真实数据集。医学专家发现这种可视化方法很有趣，因为它通过共享患者特征的可视化解释了为什么病例相似。