An Integrated Approach to Stage 1 Breast Cancer Detection

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@InProceedings{Fitzgerald:2015:GECCO,
  author =       "Jeannie M. Fitzgerald and Conor Ryan and 
                 David Medernach and Krzysztof Krawiec",
  title =        "An Integrated Approach to Stage 1 Breast Cancer
                 Detection",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1199--1206",
  keywords =     "genetic algorithms, genetic programming, Real World
                 Applications",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754761",
  DOI =          "doi:10.1145/2739480.2754761",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We present an automated, end-to-end approach for
                 Stage~1 breast cancer detection. The first phase of our
                 proposed work-flow takes individual digital mammograms
                 as input and outputs several smaller sub-images from
                 which the background has been removed. Next, we extract
                 a set of features which capture textural information
                 from the segmented images.

                 In the final phase, the most salient of these features
                 are fed into a Multi-Objective Genetic Programming
                 system which then evolves classifiers capable of
                 identifying those segments which may have suspicious
                 areas that require further investigation.

                 A key aspect of this work is the examination of several
                 new experimental configurations which focus on textural
                 asymmetry between breasts. The best evolved classifier
                 using such a configuration can deliver results of
                 100percent accuracy on true positives and a false
                 positive per image rating of just 0.33, which is better
                 than the current state of the art.",
  notes =        "Also known as \cite{2754761} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

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

Citations