- 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)",
