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Characterizing ResNet Filters to Identify Positive and Negative Findings in Breast MRI Sequences

Abstract:

Training of deep learning models requires large and properly labeled datasets, which make unfeasible using it for developing computer-aided diagnosis systems in medical imaging. As an alternative, transfer learning has shown to be useful to extract deep features using architectures previously trained. In this paper, a new method for classification of breast lesions in magnetic resonance imaging is proposed, which uses the pre-trained ResNet-50 architecture for extracting a set of image features that are then used by an SVM model for differentiating between positive and negative findings. We take advantage of the ResNet-50 architecture for introducing volumetric lesion information by including three consecutive slices per lesion. Filters used as feature descriptors were selected using a multiple kernel learning method, which allows identifying those filters that provide the most relevant information for the classification task. Additionally, instead of using raw filters as features, we propose to characterize it using statistical moments, which improves the classification performance. The evaluation was conducted using a set of 146 ROIs extracted from three sequences proposed for designing abbreviated breast MRI protocols (DCE, ADC, and T2-Vista). Positive findings were identified with an AUC of 82.4 using a DCE image, and 81.08 fusing features from the three sequences.