Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5D information

Zhikai Yang; Tianyu Fan; Örjan Smedby; Rodrigo Moreno

doi:10.1117/12.3005496

3 April 2024 Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5D information

Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270G (2024) https://doi.org/10.1117/12.3005496
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhikai Yang, Tianyu Fan, Örjan Smedby, and Rodrigo Moreno "Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5D information", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270G (3 April 2024); https://doi.org/10.1117/12.3005496

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