Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Machine Learning
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Why Case-Based Reasoning Is Attractive for Image Interpretation
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Protein structure prediction with visuospatial analogy
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
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The kind of cells considered in this applications are Hep- 2 cells, which get used for the identification of antinuclear autoantibodies (ANA). Hep-2 cells allow recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns is recently done manually by a human inspecting the slides with a microscope. In the paper, we present first results on image analysis, feature extraction and classification. Starting from a knowledge acquisition process with a human operator, we developed image analysis and feature extraction algorithm. A data set containing 112 features for each entry was set up and given to machine learning techniques to find out the relevant features among this large feature set and to construct the structure of the classifier. The classifier was evaluated by crossvalidation method. The results are good and show the feasibility of an automatic inspection system.