eXplainable Artificial Intelligence on Medical Images: A Survey

When it comes to artificial intelligence (AI) tasks, deep learning systems--exemplified by deep neural networks--are quickly becoming the industry standard [1]. This includes everything from language comprehension and speech/image recognition to machine translation and planning, and even game playing and autonomous driving. Therefore, familiarity with deep learning is rapidly evolving from a specialized plus to a necessary requirement in many elite academic settings and a significant competitive advantage in the business world's job market. The "black box" concept, wherein Deep Neural Networks are said to lack transparency or interpretability of how input data are transformed into model outputs, is a major concern for the widespread application of Deep Neural Networks [2, 3]. Many nonlinear, intertwined relations connect the various "layers" in a neural network. It is unrealistic to expect to understand the neural network's decision-making process even after inspecting all these layers and describing their relations. The lack of interpretability is causing growing concern across a variety of application domains because it can have far-reaching and unintended consequences. Medical imaging is one area where deploying AI models is met with skepticism due to the high stakes involved in a wrong classification [4, 5]. This paper reflects on recent investigations regarding the interpretability and explainability of Deep Learning methods.