A qualitative theory of uncertainty
Fundamenta Informaticae
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Computer-assisted diagnosis system in digestive endoscopy
IEEE Transactions on Information Technology in Biomedicine
Possibilistic evidential clustering
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Digestive casebase mining based on possibility theory and linear unidimensional scaling
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Possibilistic pattern recognition in a digestive database for mining imperfect data
WSEAS TRANSACTIONS on SYSTEMS
Possibilistic Similarity Estimation and Visualization
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Imperfect pattern recognition using the fuzzy measure theory
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Gastroenterology dataset clustering using possibilistic Kohonen maps
WSEAS Transactions on Information Science and Applications
Towards a possibilistic classification of gastroenterology patterns in a complex environment
WSEAS TRANSACTIONS on SYSTEMS
| Hi-index | 0.00 |
Provided with evolved functionalities, a digestive endoscopy atlas can be used as a tool of training and even of diagnosis aid. The architecture of such a system is leaning on two bases, one of endoscopic knowledge another of case iconography. Being inspired by medical practice, a bi-leveled - disease knowledge base allows a classification of possible diagnoses and a case selection of the endoscopic case base, enabling the similarity step to complete the retrieval. This project benefits at many levels from the "pixelization paradigm". Indeed, to visualize the Knowledge and Case bases is of great interest, but it's more exciting to visualize the steps of the classification and of the similar case retrieval by the generation of images confronting the knowledge base and the case base to the new case.