Discriminative Localized Sparse Representations for Breast Cancer Screening
This work aims to improve the accuracy and reproducibility of breast cancer screening for medical professionals, but the abstract does not provide concrete performance metrics to assess its impact.
This paper proposes a method called Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA) for classifying breast lesions as benign or malignant. The method, when combined with LC-KSVD dictionary learning, was evaluated using 10-, 20-, and 30-fold cross-validation on the MIAS dataset.
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.