Multi-view DBT Grid-Attention Detection Framework
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.
