Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details.

Grid infrastructures for developing mammography CAD systems.

This paper presents a set of technologies developed to exploit Grid infrastructures for breast cancer CAD, that include (1) federated repositories of mammography images and clinical data over Grid storage, (2) a workstation for mammography image analysis and diagnosis and (3) a framework for data analysis and training machine learning classifiers over Grid computing power specially tuned for medical image based data. An experimental mammography digital repository of approximately 300 mammograms from the MIAS database was created and classifiers were built achieving a 0.85 average area under the ROC curve in a dataset of 100 selected mammograms with representative pathological lesions and normal cases.