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.
@INPROCEEDINGS { ryan:2014:EuroGP-short,
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. },
ADDRESS = { Granada, Spain },
AUTHOR = { Conor Ryan and Krzysztof Krawiec and Una-May O'Reilly and Jeannie Fitzgerald and David Medernach },
BOOKTITLE = { 17th European Conference on Genetic Programming },
DOI = { doi:10.1007/978-3-662-44303-3_14 },
EDITOR = { Miguel Nicolau et al. },
ISBN13 = { 978-3-662-44302-6 },
KEYWORDS = { genetic algorithms, genetic programming },
MONTH = { {23-25 } # apr },
NOTES = { Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014 },
ORGANISATION = { EvoStar },
PAGES = { 162--173 },
PUBLISHER = { Springer },
SERIES = { LNCS },
TITLE = { Building a Stage 1 Computer Aided Detector for Breast Cancer using Genetic Programming },
VOLUME = { 8599 },
YEAR = { 2014 },
1 = { https://doi.org/10.1007/978-3-662-44303-3_14 },
}