Authors:
Priya Deshpande
;
Alexander Rasin
;
Fang Cao
;
Sriram Yarlagadda
;
Eli Brown
;
Jacob Furst
and
Daniela S. Raicu
Affiliation:
College of Computing and Digital Media, DePaul University, Chicago and U.S.A.
Keyword(s):
Information Retrieval, Relevance Ranking, Radiology Teaching Files Database, Medical Ontology, Content based Image Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Radiology teaching files serve as a reference in the diagnosis process and as a learning resource for radiology residents. Many public teaching file data sources are available online and private in-house repositories are maintained in most hospitals. However, the native interfaces for querying public repositories have limited capabilities. The Integrated Radiology Image Search (IRIS) Engine was designed to combine public data sources and in-house teaching files into a single resource. In this paper, we present and evaluate ranking strategies that prioritize the most relevant teaching files for a query. We quantify query context through a weighted text-based search and with ontology integration. We also incorporate an image-based search that allows finding visually similar teaching files. Finally, we augment text-based search results with image-based search – a hybrid approach that further improves search result relevance. We demonstrate that this novel approach to searching radiology
data produces promising results by evaluating it with an expert panel of reviewers and by comparing our search performance against other publicly available search engines.
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