KEYWORDS: Deformation, Digital breast tomosynthesis, Object detection, Transformers, Convolution, Breast, Feature extraction, Education and training, Performance modeling, Information fusion
In this study, we adapted a transformer-based method to localize lesions in digital breast tomosynthesis (DBT) images. Compared with convolutional neural network-based object detection methods, the transformer-based method does not require non-maximum suppression postprocessing. Integrated deformable convolution detection transformers can better capture small-size lesions. We added transfer learning to tackle the issue of the lack of annotated data from DBT. To validate the superiority of the transformer-based detection method, we compared the results with deep-learning object detection methods. The experimental results demonstrated that the proposed method performs better than all comparison methods.
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