Document Type
Article
Publication Date
8-2025
Publisher
Elsevier
Abstract
This study introduces a novel multimodal deep learning model tailored for the differentiation of benign and malignant breast masses using dual-view breast ultrasound images (radial and anti-radial views) in conjunction with corresponding radiology reports. The proposed multimodal model architecture includes specialized image and text encoders for independent feature extraction, along with a transformation layer to align the multimodal features for the subsequent classification task. The model achieved an area of the curve of 85% and outperformed unimodal models with 6% and 8% in Youden index. Additionally, our multimodal model surpassed zero-shot predictions generated by prominent foundation models such as CLIP and MedCLIP. In direct comparison with classification results based on physician-assessed ratings, our model exhibited clear superiority, highlighting its practical significance in diagnostics. By integrating both image and text modalities, this study exemplifies the potential of multimodal deep learning in enhancing diagnostic performance, laying the foundation for developing robust and transparent AI-assisted solutions.
Recommended Citation
Wei, T.-R., Chang, A., Kang, Y., Patel, M., Fang, Y., & Yan, Y. (2025). Multimodal deep learning for enhanced breast cancer diagnosis on sonography. Computers in Biology and Medicine, 194, 110466. https://doi.org/10.1016/j.compbiomed.2025.110466
Comments
Open access to this article is funded by Santa Clara University Library.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).