  Deep neural networks (DNNs) detect patterns in data and have shown
versatility and strong performance in many computer vision applications.
However, DNNs alone are susceptible to obvious mistakes that violate simple,
common sense concepts and are limited in their ability to use explicit
knowledge to guide their search and decision making. While overall DNN
performance metrics may be good, these obvious errors, coupled with a lack of
explainability, have prevented widespread adoption for crucial tasks such as
medical image analysis. The purpose of this paper is to introduce SimpleMind,
an open-source software framework for Cognitive AI focused on medical image
understanding. It allows creation of a knowledge base that describes expected
characteristics and relationships between image objects in an intuitive
human-readable form. The SimpleMind framework brings thinking to DNNs by: (1)
providing methods for reasoning with the knowledge base about image content,
such as spatial inferencing and conditional reasoning to check DNN outputs; (2)
applying process knowledge, in the form of general-purpose software agents,
that are chained together to accomplish image preprocessing, DNN prediction,
and result post-processing, and (3) performing automatic co-optimization of all
knowledge base parameters to adapt agents to specific problems. SimpleMind
enables reasoning on multiple detected objects to ensure consistency, providing
cross checking between DNN outputs. This machine reasoning improves the
reliability and trustworthiness of DNNs through an interpretable model and
explainable decisions. Example applications are provided that demonstrate how
SimpleMind supports and improves deep neural networks by embedding them within
a Cognitive AI framework.
