A residual-attention multimodal fusion network (ResAMF-Net) for detection and classification of breast cancer

Xuan Liu; Yinhao Ren; Marc Ryser; Lars J. Grimm; Joseph Y. Lo

doi:10.1117/12.3006806

3 April 2024 A residual-attention multimodal fusion network (ResAMF-Net) for detection and classification of breast cancer

Xuan Liu, Yinhao Ren, Marc Ryser, Lars J. Grimm, Joseph Y. Lo

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271Z (2024) https://doi.org/10.1117/12.3006806
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

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

Citation Download Citation

Xuan Liu, Yinhao Ren, Marc Ryser, Lars J. Grimm, and Joseph Y. Lo "A residual-attention multimodal fusion network (ResAMF-Net) for detection and classification of breast cancer", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271Z (3 April 2024); https://doi.org/10.1117/12.3006806

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