Achievements and Challenges in Explaining Deep Learning based
  Computer-Aided Diagnosis Systems
  Remarkable success of modern image-based AI methods and the resulting
interest in their applications in critical decision-making processes has led to
a surge in efforts to make such intelligent systems transparent and
explainable. The need for explainable AI does not stem only from ethical and
moral grounds but also from stricter legislation around the world mandating
clear and justifiable explanations of any decision taken or assisted by AI.
Especially in the medical context where Computer-Aided Diagnosis can have a
direct influence on the treatment and well-being of patients, transparency is
of utmost importance for safe transition from lab research to real world
clinical practice. This paper provides a comprehensive overview of current
state-of-the-art in explaining and interpreting Deep Learning based algorithms
in applications of medical research and diagnosis of diseases. We discuss early
achievements in development of explainable AI for validation of known disease
criteria, exploration of new potential biomarkers, as well as methods for the
subsequent correction of AI models. Various explanation methods like visual,
textual, post-hoc, ante-hoc, local and global have been thoroughly and
critically analyzed. Subsequently, we also highlight some of the remaining
challenges that stand in the way of practical applications of AI as a clinical
decision support tool and provide recommendations for the direction of future
research.
