A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. However, adding graph features to an existing computer-aided diagnosis (CAD) pipeline incurs an increase of feature space dimensionality, which poses additional challenges to traditional supervised machine learning techniques due to the inability to increase accordingly the number of training datasets. We propose the combination of unsupervised dimensionality reduction and embedded space clustering followed by a supervised classifier to improve the performance of a CAD system for nonmass-like lesions in breast MRI. Our work extends a previoulsy proposed framework for deep embedded unsupervised clustering (DEC) to embedding space classification, with the joint optimization of objective functions for DEC and supervised multi-layered perceptron (MLP) classification. The strength of the method lies in the ability to learn and further optimize an embedded feature representation of lower dimensionality that maximizes the diagnostic accuracy of a CAD lesion classifier to discriminate between benign and malignant lesions. We identified 792 nonmass-like enhancements (267 benign, 110 malignant and 415 unknown) in 411 patients undergoing breast MRI at our institution. The diagnostic performance of the proposed method was evaluated and compared to the performance of a conventional supervised MLP classifier in original feature space. A statistically significant increase in diagnostic area under the ROC curve (AUC) was achieved. Generalization AUC increased from 0.67 ± 0.08 to 0.81 ± 0.10 (21% increase, p-value=4.2×10) with the proposed graph-based lesion characterization and deep embedding framework.