TY - GEN
T1 - Multimodal Breast Ultrasound Segmentation
T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
AU - Mendez, Armando
AU - Xu, Meng
AU - Huang, Kuan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide, highlighting the critical need for early detection and accurate diagnosis. Breast ultrasound (BUS) imaging is one of the most essential methods for early diagnosis of breast cancer. In this research, we develop a novel hybrid U-shaped network for the automated segmentation of breast lesions in BUS images. Meanwhile, we employ a multimodal approach that combines visual features from ultrasound images with contextual textual information, improving the model’s understanding of tumor characteristics. We evaluate various configurations, including selecting clinical features such as tumor classification and BI-RADS scores. Our findings show that the proposed multimodal framework outperforms some transformer-based models on a public BUS image dataset, achieving significant advancements. This study underscores the effectiveness of multimodal learning in medical image analysis and highlights the potential of transformer-language models to improve diagnostic tools for early breast cancer detection.
AB - Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide, highlighting the critical need for early detection and accurate diagnosis. Breast ultrasound (BUS) imaging is one of the most essential methods for early diagnosis of breast cancer. In this research, we develop a novel hybrid U-shaped network for the automated segmentation of breast lesions in BUS images. Meanwhile, we employ a multimodal approach that combines visual features from ultrasound images with contextual textual information, improving the model’s understanding of tumor characteristics. We evaluate various configurations, including selecting clinical features such as tumor classification and BI-RADS scores. Our findings show that the proposed multimodal framework outperforms some transformer-based models on a public BUS image dataset, achieving significant advancements. This study underscores the effectiveness of multimodal learning in medical image analysis and highlights the potential of transformer-language models to improve diagnostic tools for early breast cancer detection.
KW - Breast Ultrasound
KW - Image Segmentation
KW - Multimodality
KW - Transformer
UR - https://www.scopus.com/pages/publications/105014422775
U2 - 10.1007/978-3-031-94962-3_16
DO - 10.1007/978-3-031-94962-3_16
M3 - Conference contribution
AN - SCOPUS:105014422775
SN - 9783031949616
T3 - Communications in Computer and Information Science
SP - 177
EP - 187
BT - Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Proceedings
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Shenavarmasouleh, Farzan
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 December 2024 through 13 December 2024
ER -