Sparse analysis of deep features for characterization of breast masses

Breast cancer is the second most common type of cancer of women in the U.S. behind skin cancer. Early detection and characterization of breast masses is critical for effective diagnosis and treatment of breast cancer. Computer-aided breast mass characterization methods would help to improve the accuracy of diagnoses, their reproducibility, and the throughput of breast cancer screening workflows. In this work, we introduce sparse representations of deep learning features for separation of malignant from benign breast masses in mammograms. We expect that the use of deep feature-based dictionaries will produce better benign/malignant class separation than straightforward sparse representation techniques, and fine-tuned convolutional neural networks (CNNs). We performed 10- and 30-fold cross-validation experiments for classification of benign and malignant breast masses on the MIAS and DDSM mammographic datasets. The results show that the proposed deep feature sparse analysis produces better classification rates than conventional sparse representations and fine-tuned CNNs. The top areas under the curve (AUC) for the receiver operating curve are 80.64% for 10-fold and 97.44% for 30-fold cross-validation in MIAS, and 77.29% for 10-fold and 76.02% for 30-fold cross-validation in DDSM. The main advantages of this approach are that it employs dictionaries of deep network features that are sparse in nature and that it alleviates the need for large volumes of training data and lengthy training procedures. The interesting results from this work prompt further exploration of the relationship between sparse optimization problems and deep learning.