Ontology-based medical image annotation with description logics
B. Hu, S. Dasmahapatra, P. Lewis, and N. Shadbolt. Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE
International Conference on, page 77--82. (2003)
Abstract
The interpretation of medical evidence is normally presented in terms
of a controlled, but diversely expressed specialist vocabulary and
natural language phrases. Such informally expressed data require
human intervention to ascertain its relevance in any specific case.
In order to facilitate machine-based reasoning about the evidence
gathered, additional interpretive semantics must be attached to the
data; a shift from a merely data-intensive approach to a semantics-rich
model of evidence. In this paper, we present a system to formally
annotate medical images captured to aid the diagnosis and management
of breast cancer, that enables a series of semantics-based operations
to be performed. Our approach is grounded upon an imaging ontology
specifying the domain knowledge and a description logic (DL) taxonomic
inferential engine responsible for semantics-based reasoning and
image retrieval.
%0 Conference Paper
%1 Hu2003
%A Hu, Bo
%A Dasmahapatra, S.
%A Lewis, P.
%A Shadbolt, N.
%B Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE
International Conference on
%D 2003
%K annotation, approach, breast cancer computing, data-intensive description diagnosis, diagnostic evidence image image, imaging inference information interpretation, interpretative language logics, machine-based management, mechanisms, medical model, natural ontology, ontology-based operation, phrases, processing, reasoning, retrieval, semantics, semantics-based semantics-rich specialist systems, vocabulary
%P 77--82
%T Ontology-based medical image annotation with description logics
%X The interpretation of medical evidence is normally presented in terms
of a controlled, but diversely expressed specialist vocabulary and
natural language phrases. Such informally expressed data require
human intervention to ascertain its relevance in any specific case.
In order to facilitate machine-based reasoning about the evidence
gathered, additional interpretive semantics must be attached to the
data; a shift from a merely data-intensive approach to a semantics-rich
model of evidence. In this paper, we present a system to formally
annotate medical images captured to aid the diagnosis and management
of breast cancer, that enables a series of semantics-based operations
to be performed. Our approach is grounded upon an imaging ontology
specifying the domain knowledge and a description logic (DL) taxonomic
inferential engine responsible for semantics-based reasoning and
image retrieval.
@inproceedings{Hu2003,
abstract = {The interpretation of medical evidence is normally presented in terms
of a controlled, but diversely expressed specialist vocabulary and
natural language phrases. Such informally expressed data require
human intervention to ascertain its relevance in any specific case.
In order to facilitate machine-based reasoning about the evidence
gathered, additional interpretive semantics must be attached to the
data; a shift from a merely data-intensive approach to a semantics-rich
model of evidence. In this paper, we present a system to formally
annotate medical images captured to aid the diagnosis and management
of breast cancer, that enables a series of semantics-based operations
to be performed. Our approach is grounded upon an imaging ontology
specifying the domain knowledge and a description logic (DL) taxonomic
inferential engine responsible for semantics-based reasoning and
image retrieval.},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Hu, Bo and Dasmahapatra, S. and Lewis, P. and Shadbolt, N.},
biburl = {https://www.bibsonomy.org/bibtex/2bf0c904972a568501aee5c069db5ef10/mozaher},
booktitle = {Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE
International Conference on},
file = {01250173.pdf:Hu2003.pdf:PDF},
interhash = {2b2222ae7127918995aff4c4c41ff033},
intrahash = {bf0c904972a568501aee5c069db5ef10},
issn = {1082-3409},
keywords = {annotation, approach, breast cancer computing, data-intensive description diagnosis, diagnostic evidence image image, imaging inference information interpretation, interpretative language logics, machine-based management, mechanisms, medical model, natural ontology, ontology-based operation, phrases, processing, reasoning, retrieval, semantics, semantics-based semantics-rich specialist systems, vocabulary},
owner = {mozaher},
pages = {77--82},
timestamp = {2009-09-12T19:19:39.000+0200},
title = {Ontology-based medical image annotation with description logics},
year = 2003
}