![]() ![]() The representation is based on semantic networks, originally developed and applied in language processing, and is intuitive and allows easy creation, extension, and re-use of knowledge. In a SimpleMind application for a particular type of image, the knowledge base defines what is known and can be considered “long term memory”. This stands in contrast to implicit (“black box”) knowledge encoded within DNN weights or in pre/post processing code. SimpleMind adds reasoning to deep neural networks using a knowledge base that is explicit, human-readable, and decoupled from processing algorithms. The SimpleMind environment brings thinking to DNNs by: The purpose of this paper is to introduce SimpleMind, an open-source software environment for image understanding, i.e., segmenting and recognizing image elements to form a coherent high-level model of a scene where reasoning can be performed. We provide an implementation of “thinking” that encompasses both learning and reasoning. Cognitive AI includes not only the learning of patterns in data, but also learning through teaching and concepts (declared knowledge) as well as reasoning to apply this knowledge to guide the interpretation of a specific image. To improve computer vision accuracy and reliability we embed deep neural networks within a Cognitive AI environment to meld pattern recognition with conceptual knowledge and reasoning. 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. DNNs are susceptible to obvious (“dumb”) mistakes that violate simple common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. ĭNNs can be considered a “stimulus-response” function without the thinking, using knowledge and reasoning, that makes human vision superior. Medical researchers have pointed to potential overestimation of DNN performance, diminished DNN performance on external datasets, lack of consistent performance across cohorts, and the need to build trustworthy AI. However, despite many research publications there has not been broad adoption of artificial intelligence (AI) in crucial tasks such as medical imaging. 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.ĭeep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. 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. ![]() 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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