You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
15 February 2021Sparse 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.
The alert did not successfully save. Please try again later.
Sokratis Makrogiannis, Keni Zheng, Chelsea Harris, "Sparse analysis of deep features for characterization of breast masses," Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970E (15 February 2021); https://doi.org/10.1117/12.2582321