![]() This restriction is imposed by Radiological Sciences at UCLA. “Portions of the data used for kidney and chest x-ray applications are collected from UCLA and restricted for sharing because of the sensitive patient information. Public data used for the kidney application is available at. Public data used for the prostate application is available at. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The open source software is available on GitLab. Received: DecemAccepted: MaPublished: April 13, 2023Ĭopyright: © 2023 Choi et al. National University of Sciences and Technology NUST, PAKISTAN Proof-of-principle example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI environment.Ĭitation: Choi Y, Wahi-Anwar MW, Brown MS (2023) SimpleMind: An open-source software environment that adds thinking to deep neural networks. ![]() 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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