We describe a fully automated workflow for performing stage~1 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 {\em suspicious} areas that require further investigation. A too conservative approach degenerates to marking every mammogram (or {\em 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 100\% 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 { krawiec:2014:EuroGP,
ABSTRACT = { We describe a fully automated workflow for performing stage~1 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 {\em suspicious} areas that require further investigation. A too conservative approach degenerates to marking every mammogram (or {\em 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 100\% 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 = { Genetic Programming },
DOI = { 10.1007/978-3-662-44303-3_14 },
EDITOR = { Nicolau, Miguel and Krawiec, Krzysztof and Heywood, MalcolmI. and Castelli, Mauro and Garc{\'\i}a-S{\'a}nchez, Pablo and Merelo, JuanJ. and Rivas Santos, VictorM. and Sim, Kevin },
ISBN = { 978-3-662-44302-6 },
KEYWORDS = { Genetic Programming; Classification; Mammography },
LOCATION = { Heidelberg },
MONTH = { 23-25 April },
NOTES = { Proceedings of the 17th European Conference on Genetic Programming, EuroGP 2014 },
ORGANISATION = { EvoStar },
PAGES = { 162-173 },
PUBLISHER = { Springer Berlin Heidelberg },
SERIES = { Lecture Notes in Computer Science },
TITLE = { Building a Stage 1 Computer Aided Detector for Breast Cancer using Genetic Programming },
URL = { http://dx.doi.org/10.1007/978-3-662-44303-3_14 },
VOLUME = { 8599 },
YEAR = { 2014 },
1 = { http://dx.doi.org/10.1007/978-3-662-44303-3_14 },
}