A Simple General Algorithm for the Diagnosis Explanation of Computer-Aided Diagnosis Systems in Terms of Natural Language Primitives

Abstract

A very simple algorithm for explaining decisions of cancer computer-aided diagnosis systems is proposed. The algorithm produces explanations of diseases in the form of special sentences via natural language. It consists of two parts. The first part is implemented by using a standard local post-hoc explanation model, for example, the well-known method LIME. This part aims to select important features from a segmented and detected suspicious object or its low-dimensional feature representation. The second part is a set of simple classifiers which transform the selected important features into one of the classes corresponding to simple phrases. The explanation sentences in natural language are composed of the simple phrases (primitives) such that every subset of the phrases describes a single peculiarity of a suspicious object, for example, a shape, a structure, inclusions, contours. The primitives are regarded as classes for the set of classifiers. The proposed algorithm is general and can be applied to implementing the explanation subsystem of various diseases.