KEYWORDS: Digital breast tomosynthesis, Feature fusion, Cancer, Computer aided detection, Breast cancer, Image fusion, Cancer detection, Mammography, Feature extraction, Digital mammography
Digital breast tomosynthesis (DBT), synthetic mammography, and full-field digital mammography (FFDM) are commonly used medical imaging modalities for breast cancer screening. Due to the data complexity, most CAD research applies to only one modality, which under-utilizes the complementary information in these 2D and 3D modalities. In this study, we propose a Residual-Attention Multimodal Fusion network (ResAMF-Net) that integrates lesion features across these modalities. We evaluated network performance on a large private dataset, which contains 769 cancer cases and 1375 noncancer cases (including 362 benign and 1013 normal cases) for a total of 2144 cases. At 90% case sensitivity, ResAMF-Net increases specificity by 8%, which can substantially improve radiologist workflow because almost all screening cases are true negatives.
Most of the existing CAD frameworks for digital breast tomosynthesis (DBT) are single-view only, while radiologists typically utilize information from multiple screening views to better detect breast cancer lesions. Previously, we developed the Retina-Match framework for lesion detection that performed ipsilateral matching between CC and MLO views of the same breast. In this work, we improve that framework in both sampling strategy and feature extraction processes. We proposed a “hard negative” sampling strategy to train on more difficult ipsilateral lesion pairs to increase the robustness of the matching model. We introduced a grid-attention (GA) module to apply spatial attention mechanism for ipsilateral patch similarity learning. A screening DBT dataset with 4182 cases including 1498 (36%) cancers were used for training and testing. Case specificity improved by 15% at 96% sensitivity and average False Positive numbers per detection image (FPPI) decreased from 0.6 to 0.5 at 95% case sensitivity. These experiments indicated that proper ipsilateral matching result is the key to improve performance of multi-view lesion detection framework.
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