Data mining techniques for AFM- based tumor classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Stephan Hutterer and Gerald Zauner and 
                 Marlene Huml and Rene Silye and Kurt Schilcher",
  title =        "Data mining techniques for {AFM-} based tumor
  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