Image Classification with Genetic Programming: Building a Stage 1 Computer Aided Detector for Breast Cancer

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

  author =       "Conor Ryan and Jeannie Fitzgerald and 
                 Krzysztof Krawiec and David Medernach",
  title =        "Image Classification with Genetic Programming:
                 Building a Stage 1 Computer Aided Detector for Breast
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "10",
  pages =        "245--287",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_10",
  abstract =     "This chapter describes a general approach for image
                 classification using Genetic Programming (GP) and
                 demonstrates this approach through the application of
                 GP to the task of stage 1 cancer detection in digital
                 mammograms. We detail an automated work-flow that
                 begins with image processing and culminates in the
                 evolution of classification models which identify
                 suspicious segments of mammograms. Early detection of
                 breast cancer is directly correlated with survival of
                 the disease and mammography has been shown to be an
                 effective tool for early detection, which is why many
                 countries have introduced national screening programs.
                 However, this presents challenges, as such programs
                 involve screening a large number of women and thus
                 require more trained radiologists at a time when there
                 is a shortage of these professionals in many
                 countries.Also, as mammograms are difficult to read and
                 radiologists typically only have a few minutes
                 allocated to each image, screening programs tend to be
                 conservative—involving many callbacks which increase
                 both the workload of the radiologists and the stress
                 and worry of patients.Fortunately, the relatively
                 recent increase in the availability of mammograms in
                 digital form means that it is now much more feasible to
                 develop automated systems for analysing mammograms.
                 Such systems, if successful could provide a very
                 valuable second reader function.We present a work-flow
                 that begins by processing digital mammograms to segment
                 them into smaller sub-images and to extract features
                 which describe textural aspects of the breast. The most
                 salient of these features are then used in a GP system
                 which generates classifiers capable of identifying
                 which particular segments may have suspicious areas
                 requiring further investigation. An important objective
                 of this work is to evolve classifiers which detect as
                 many cancers as possible but which are not overly
                 conservative. The classifiers give results of 100 %
                 sensitivity and a false positive per image rating of
                 just 0.33, 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

Genetic Programming entries for Conor Ryan Jeannie Fitzgerald Krzysztof Krawiec David Medernach