Building a Stage 1 Computer Aided Detector for Breast Cancer using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Conor Ryan and Krzysztof Krawiec and 
                 Una-May O'Reilly and Jeannie Fitzgerald and David Medernach",
  title =        "Building a Stage 1 Computer Aided Detector for Breast
                 Cancer using Genetic Programming",
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "162--173",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "doi:10.1007/978-3-662-44303-3_14",
  abstract =     "We describe a fully automated workflow for performing
                 stage1 breast cancer detection with GP as its
                 cornerstone. Mammograms are by far the most widely used
                 method for detecting breast cancer in women, and its
                 use in national screening can have a dramatic impact on
                 early detection and survival rates. With the increased
                 availability of digital mammography, it is becoming
                 increasingly more feasible to use automated methods to
                 help with detection. A stage 1 detector examines
                 mammograms and highlights suspicious areas that require
                 further investigation. A too conservative approach
                 degenerates to marking every mammogram (or segment of)
                 as suspicious, while missing a cancerous area can be
                 disastrous. Our workflow positions us right at the data
                 collection phase such that we generate textural
                 features ourselves. These are fed through our system,
                 which performs PCA on them before passing the most
                 salient ones to GP to generate classifiers. The
                 classifiers give results of 100percent accuracy on true
                 positives and a false positive per image rating of just
                 1.5, which is better than prior work. Not only this,
                 but our system can use GP as part of a feedback loop,
                 to both select and help generate further features.",
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",

Genetic Programming entries for Conor Ryan Krzysztof Krawiec Una-May O'Reilly Jeannie Fitzgerald David Medernach