Two algorithms for explaining decisions of a lung cancer computer-aided diagnosis system are proposed. Their main peculiarity is that they produce explanations of diseases in the form of special sentences via natural language. The algorithms consist of two parts. The first part is a standard local post-hoc explanation model, for example, the well-known LIME, which is used for selecting important features from a special feature representation of the segmented lung suspicious objects. This part is identical for both algorithms. The second part is a model which aims to connect selected important features and to transform them to explanation sentences in natural language. This part is implemented differently for both algorithms. The training phase of the first algorithm uses a special vocabulary of simple phrases which produce sentences and their embeddings. The second algorithm significantly simplifies some parts of the first algorithm and reduces the explanation problem to a set of simple classifiers. The basic idea behind the improvement is to represent every simple phrase from vocabulary as a class of the “sparse” histograms. An implementation of the second algorithm is shown in detail.

提出了两种解释肺癌计算机辅助诊断系统决策的算法。它们的主要特点是它们通过自然语言以特殊句子的形式产生对疾病的解释。算法由两部分组成。第一部分是标准的局部事后解释模型，例如著名的 LIME，用于从分割后的肺部可疑对象的特殊特征表示中选择重要特征。这部分对于两种算法是相同的。第二部分是一个模型，旨在连接选定的重要特征并将它们转换为自然语言的解释句。对于这两种算法，这部分的实现方式不同。第一个算法的训练阶段使用简单短语的特殊词汇表，这些词汇表生成句子及其嵌入。第二种算法显着简化了第一种算法的某些部分，并将解释问题简化为一组简单的分类器。改进背后的基本思想是将词汇表中的每个简单短语表示为一类“稀疏”直方图。详细示出了第二算法的实现。