The role of artificial intelligence, knowledge and wisdom in automatic image understanding

In the paper, the roles of intelligence, knowledge, learning and wisdom are discussed in the context of image content understanding. The known model of automatic image understanding is extended by the role of learning. References to example implementations are also given

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences

To Integrate The Benefits Of Statistical Methods Into Syntactic Pattern Recognition, A Bridging Approach Is Proposed: (I) Acquisition Of A Grammar Per Recognition Class (Ii) Comparison Of The Obtained Grammars In Order To Find Substructures Of Interest Represented As Sequences Of Terminal And/Or Non-Terminal Symbols And Filling The Feature Vector With Their Counts (Iii) Hierarchical Feature Selection And Hierarchical Classification, Deducing And Accounting For The Domain Taxonomy. The Bridging Approach Has The Benefits Of Syntactic Methods: Preserves Structural Relations And Gives Insights Into The Problem. Yet, It Does Not Imply Distance Calculations And, Thus, Saves A Non-Trivial Task-Dependent Design Step. Instead It Relies On Statistical Classification From Many Features. Our Experiments Concern A Difficult Problem Of Chemical Toxicity Prediction. The Code And The Data Set Are Open-Source. (C) 2015 Elsevier Ltd. All Rights Reserved

GRAPH IMAGE LANGUAGES IN SEMANTIC DESCRIPTION OF SPATIAL CORONARY ARTERIES STRUCTURE

In this paper has been proposed developing the new syntactic – semantic meaning descriptionof spatial coronary arteries structure. Thanks such description will be possible to makeessentially steered semantic interpretation of section coronary arteries morphology, what willallow us fast identification and automatisation of lumen stricture detection. In this aim hasbeen used graph image languages based on the expansive graph grammars of edNLC type,enabling creation the universal and informative meaning description of spatial coronaryarteries structure. Application of such semantic description in the integrated modules ofintelligent systems medical diagonosis, supporting the early detection stricture which defectthe flow of oxidizing blood to given area of cardiac muscle

Graph Image Languages In Semantic Description Of Spatial Coronary Arteries Structure

In this paper has been proposed developing the new syntactic – semantic meaning descriptionof spatial coronary arteries structure. Thanks such description will be possible to makeessentially steered semantic interpretation of section coronary arteries morphology, what willallow us fast identification and automatisation of lumen stricture detection. In this aim hasbeen used graph image languages based on the expansive graph grammars of edNLC type,enabling creation the universal and informative meaning description of spatial coronaryarteries structure. Application of such semantic description in the integrated modules ofintelligent systems medical diagonosis, supporting the early detection stricture which defectthe flow of oxidizing blood to given area of cardiac muscle

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

COHORT IDENTIFICATION FROM FREE-TEXT CLINICAL NOTES USING SNOMED CT’S SEMANTIC RELATIONS

In this paper, a new cohort identification framework that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value, which was established where the best F1 score could be achieved. The performance of the proposed system was evaluated for three eligibility criteria. The current framework’s macro-average F1 score across three eligibility criteria was higher than the previously reported results of the 2018 n2c2 (0.933 vs. 0.889). This study demonstrated that SNOMED CT alone can be leveraged for cohort identification tasks without referring to external textual sources for training.Doctor of Philosoph

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"