  Breast cancer is the most common cancer among women both in developed and
developing countries. Early detection and diagnosis of breast cancer may reduce
its mortality and improve the quality of life. Computer-aided detection (CADx)
and computer-aided diagnosis (CAD) techniques have shown promise for reducing
the burden of human expert reading and improve the accuracy and reproducibility
of results. Sparse analysis techniques have produced relevant results for
representing and recognizing imaging patterns. In this work we propose a method
for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA).
In this work we apply dictionary learning to our block based sparse analysis
method to classify breast lesions as benign or malignant. The performance of
our method in conjunction with LC-KSVD dictionary learning is evaluated using
10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results
indicate that the proposed sparse analyses may be a useful component for breast
cancer screening applications.
