This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross-checking between DNN outputs. SimpleMind 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 dynamically 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. The knowledge base can then be applied to an input image to recognize and understand its content. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The purpose of this paper is to introduce SimpleMind, an open-source software environment for Cognitive AI focused on medical image understanding. 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. 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. Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications.
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