Paper Info
Title
Multimodal Ranked Search Over Integrated Repository Of Radiology Data Sources
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.
Year
DOI
Venue
2019
10.5220/0008166603720383
KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR
Keywords
Field
DocType
Information Retrieval, Relevance Ranking, Radiology Teaching Files Database, Medical Ontology, Content based Image Retrieval
Search engine,Ontology integration,Ranking,Computer science,Learning resource,Radiology,Content-based image retrieval
Conference
Volume
Citations 
PageRank 
2
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Priya Deshpande122.15
Alexander Rasin22950209.48
Fang Cao38914.98
Sriram Yarlagadda400.34
Eli T. Brown51157.05
Jacob D. Furst600.68
Daniela Stan Raicu746946.22