Data mining techniques for AFM- based tumor classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Hutterer:2012:CIBCB,

• author = "Stephan Hutterer and Gerald Zauner and Marlene Huml and Rene Silye and Kurt Schilcher",
• title = "Data mining techniques for {AFM-} based tumor classification",
• booktitle = "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2012)",
• year = "2012",
• month = "9-12 " # may,
• pages = "105--111",
• size = "7 pages",
• abstract = "The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterisation of histological brain tumour samples. Data mining techniques will be applied for automatic identification of brain tumour tissues based on AFM images by means of classifying grade II and IV tumours. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler-Poincare characteristic) as a feature descriptor to characterise global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.",
• keywords = "genetic algorithms, genetic programming, AFM-based tumour classification, Euler-Poincare characteristics, Minkowski functionals, atomic force microscopy, automatic identification, biophysics, biotechnology, brain tumour tissues, data mining techniques, electrochemistry, feature analysis, feature descriptor, feature reduction, global geometric structures, histological brain tumour samples, life science, materials science, morphological characterisation, nanotechnology, polymer science, support vector machines, surface nanoscale structure topography, Poincare mapping, atomic force microscopy, brain, data mining, electrochemistry, feature extraction, image classification, medical image processing, nanomedicine, support vector machines, surface morphology, surface topography, tumours",
• DOI = "doi:10.1109/CIBCB.2012.6217218",
• notes = "Also known as \cite{6217218}",

}

Genetic Programming entries for Stephan Hutterer Gerald Zauner Marlene Huml Rene Silye Kurt Schilcher

Citations