Computer Aided Diagnosis Systems Using Explainable AI for Digital Pathology

Abstract

With the recent advance of deep learning technology, research of artificial intelligence (AI) applications for the medical domain has been growing. However, a lack of interpretability (or explainability) of the basis of AI decisions is a major problem for practical use of AI. Since physicians are responsible for the diagnosis, it is desirable to be able to confirm the reliability of the decisions made by AI. We developed an XAI technique to realize an AI platform, which allows co-evolve both humans and AI via interaction between them and applied it to pathological image analysis.