We introduce an improvement to the feature pyramid network of standard object detection models. We call our method enhanced featuRE Pyramid network by Local Image translation and Conjunct Attention, or REPLICA. REPLICA improves object detection performance by simultaneously (1) generating realistic but fake images with simulated objects to mitigate the data-hungry problem of the attention mechanism, and (2) advancing the detection model architecture through a novel modification of attention on image feature patches. Specifically, we use a convolutional autoencoder as a generator to create new images by injecting objects into images via local interpolation and reconstruction of their features extracted in hidden layers. Then due to the larger number of simulated images, we use a visual transformer to enhance outputs of each ResNet layer that serve as inputs to a feature pyramid network. We apply our methodology to the problem of detecting lesions in Digital Breast Tomosynthesis scans (DBT), a high-resolution medical imaging modality crucial in breast cancer screening. We demonstrate qualitatively and quantitatively that REPLICA can improve the accuracy of tumor detection using our enhanced standard object detection framework via experimental results.
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