From Generic Knowledge to Specific Reasoning for Medical Image Interpretation Using Graph based Representations
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 224-228.
Abstract
In several domains of spatial reasoning, such as medical image interpretation, spatial relations between structures play a crucial role since they are less prone to variability than intrinsic properties of structures. Moreover, they constitute an important part of available knowledge. We show in this paper how this knowledge can be appropriately represented by graphs and fuzzy models of spatial relations, which are integrated in a reasoning process to guide the recognition of individual structures in images. However pathological cases may deviate substantially from generic knowledge. We propose a method to adapt the knowledge representation to take into account the influence of the pathologies on the spatial organization of a set of structures, based on learning procedures. We also propose to adapt the reasoning process, using graph based propagation and updating.
@inproceedings{ijcai2007p34,
title = {From Generic Knowledge to Specific Reasoning for Medical Image Interpretation Using Graph based Representations},
author = {Atif, Jamal and Hudelot, Céline and Fouquier, Geoffroy and Bloch, Isabelle and Angelini, Elsa D.},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {224--228},
year = {2007},
url = {https://ijcai.org/proceedings/2007/34},
}
TY - CPAPER TI - From Generic Knowledge to Specific Reasoning for Medical Image Interpretation Using Graph based Representations AU - Atif, Jamal AU - Hudelot, Céline AU - Fouquier, Geoffroy AU - Bloch, Isabelle AU - Angelini, Elsa D. T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 224 EP - 228 UR - https://ijcai.org/proceedings/2007/34 ER -Copied
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