Spatial Reasoning for Plausibility Checks of Medical Object Recognition
Results Using the Foundational Model of Anatomy
M. Möller, P. Ernst, and A. Dengel. Proc. of the 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI), Kuching, Sarawak, Malaysia, (26th -- 30th 2010)
Abstract
We present a rule-based system using medical expert knowledge represented in a formal ontology to check the results of automatic medical object recognition algorithms for anatomical plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology and uses a set of forward rules executed by a Prolog engine. In our evaluation we describe how this approach can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for anatomical plausibility. Our results show that the combination of sub-symbolic object recognition, medical domain knowledge represented in formal ontologies and yields an improved overall recognition precision.
%0 Conference Paper
%1 Moeller2010a
%A Möller, Manuel
%A Ernst, Patrick
%A Dengel, Andreas
%B Proc. of the 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI)
%C Kuching, Sarawak, Malaysia
%D 2010
%K checks medical medico plausibility reasoning spatial
%T Spatial Reasoning for Plausibility Checks of Medical Object Recognition
Results Using the Foundational Model of Anatomy
%X We present a rule-based system using medical expert knowledge represented in a formal ontology to check the results of automatic medical object recognition algorithms for anatomical plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology and uses a set of forward rules executed by a Prolog engine. In our evaluation we describe how this approach can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for anatomical plausibility. Our results show that the combination of sub-symbolic object recognition, medical domain knowledge represented in formal ontologies and yields an improved overall recognition precision.
@inproceedings{Moeller2010a,
abstract = {We present a rule-based system using medical expert knowledge represented in a formal ontology to check the results of automatic medical object recognition algorithms for anatomical plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology and uses a set of forward rules executed by a Prolog engine. In our evaluation we describe how this approach can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for anatomical plausibility. Our results show that the combination of sub-symbolic object recognition, medical domain knowledge represented in formal ontologies and yields an improved overall recognition precision.},
added-at = {2010-08-01T11:56:21.000+0200},
address = {Kuching, Sarawak, Malaysia},
author = {Möller, Manuel and Ernst, Patrick and Dengel, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/2e981979d3175d4fc853e69f86b7cf9ce/patrick6585},
booktitle = {Proc. of the 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI)},
interhash = {b353bfbf5f286e3dc2a60cba9175bb01},
intrahash = {e981979d3175d4fc853e69f86b7cf9ce},
keywords = {checks medical medico plausibility reasoning spatial},
month = {26th -- 30th},
timestamp = {2010-08-01T11:56:22.000+0200},
title = {Spatial Reasoning for Plausibility Checks of Medical Object Recognition
Results Using the Foundational Model of Anatomy},
year = 2010
}