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Deep neural networks (DNNs) detect patterns in data and have shownversatility 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 explicitknowledge to guide their search and decision making. While overall DNNperformance metrics may be good, these obvious errors, coupled with a lack ofexplainability, have prevented widespread adoption for crucial tasks such asmedical image analysis. The purpose of this paper is to introduce SimpleMind,an open-source software framework for Cognitive AI focused on medical imageunderstanding. It allows creation of a knowledge base that describes expectedcharacteristics and relationships between image objects in an intuitivehuman-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 allknowledge base parameters to adapt agents to specific problems. SimpleMindenables reasoning on multiple detected objects to ensure consistency, providingcross checking between DNN outputs. This machine reasoning improves thereliability and trustworthiness of DNNs through an interpretable model andexplainable decisions. Example applications are provided that demonstrate howSimpleMind supports and improves deep neural networks by embedding them withina Cognitive AI framework.

SimpleMind adds thinking to deep neural networks