Multimodal medical information retrieval with unsupervised rank fusion

•We propose a medical retrieval system supporting text and image queries.•It can retrieve either relevant PubMed articles or images from those articles.•It supports automatic query term expansion from the MeSH thesaurus.•Novel fusion algorithm, ISR, improves results from existing rank fusion algorit...

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Published in:Computerized medical imaging and graphics Vol. 39; pp. 35 - 45
Main Authors: Mourão, André, Martins, Flávio, Magalhães, João
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.01.2015
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ISSN:0895-6111, 1879-0771, 1879-0771
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Abstract •We propose a medical retrieval system supporting text and image queries.•It can retrieve either relevant PubMed articles or images from those articles.•It supports automatic query term expansion from the MeSH thesaurus.•Novel fusion algorithm, ISR, improves results from existing rank fusion algorithms.•We got the best result on multimodal case-based retrieval in 2013 ImageCLEFMedical Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.
AbstractList Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.
Highlights • We propose a medical retrieval system supporting text and image queries. • It can retrieve either relevant PubMed articles or images from those articles. • It supports automatic query term expansion from the MeSH thesaurus. • Novel fusion algorithm, ISR, improves results from existing rank fusion algorithms. • We got the best result on multimodal case-based retrieval in 2013 ImageCLEFMedical
•We propose a medical retrieval system supporting text and image queries.•It can retrieve either relevant PubMed articles or images from those articles.•It supports automatic query term expansion from the MeSH thesaurus.•Novel fusion algorithm, ISR, improves results from existing rank fusion algorithms.•We got the best result on multimodal case-based retrieval in 2013 ImageCLEFMedical Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.
Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.
Author Mourão, André
Magalhães, João
Martins, Flávio
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Keywords Medical search
Search interfaces
Data fusion
Assisted query formulation
Multimodal retrieval
Language English
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Snippet •We propose a medical retrieval system supporting text and image queries.•It can retrieve either relevant PubMed articles or images from those articles.•It...
Highlights • We propose a medical retrieval system supporting text and image queries. • It can retrieve either relevant PubMed articles or images from those...
Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts...
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pubmed
crossref
elsevier
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Enrichment Source
Publisher
StartPage 35
SubjectTerms Algorithms
Artificial Intelligence
Assisted query formulation
Data fusion
Data integration
Documentation - methods
Health care
Information retrieval
Information Storage and Retrieval - methods
Internal Medicine
Medical
Medical search
Medical Subject Headings
Multimodal Imaging - methods
Multimodal retrieval
Natural Language Processing
Other
Pattern Recognition, Automated - methods
PubMed
Radiology Information Systems - organization & administration
Retrieval
Search interfaces
Searching
Subtraction Technique
Terminology as Topic
Thesauri
User-Computer Interface
Vocabulary, Controlled
Title Multimodal medical information retrieval with unsupervised rank fusion
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0895611114000664
https://www.clinicalkey.es/playcontent/1-s2.0-S0895611114000664
https://dx.doi.org/10.1016/j.compmedimag.2014.05.006
https://www.ncbi.nlm.nih.gov/pubmed/24909951
https://www.proquest.com/docview/1645782753
https://www.proquest.com/docview/1660387337
https://www.proquest.com/docview/1669899273
Volume 39
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