Discriminative Localized Sparse Representations for Breast Cancer Screening

TL;DR

This paper introduces a novel sparse analysis method with dictionary learning for classifying breast lesions as benign or malignant, demonstrating its potential usefulness in breast cancer screening.

Contribution

It proposes the LC-SLESA method combined with LC-KSVD dictionary learning for improved breast lesion classification.

Findings

Effective classification accuracy on MIAS dataset

Robustness across different cross-validation folds

Potential for integration into screening workflows

Abstract

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.