Open Access
| Issue |
BIO Web Conf.
Volume 237, 2026
2026 8th International Conference on Biotechnology and Biomedicine (ICBB 2026)
|
|
|---|---|---|
| Article Number | 03007 | |
| Number of page(s) | 6 | |
| Section | Biomaterials, Medical Devices and Biomedical Engineering | |
| DOI | https://doi.org/10.1051/bioconf/202623703007 | |
| Published online | 10 June 2026 | |
- Dan, Q., et al., Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions. Cancers (Basel), 2023. 15(7). [Google Scholar]
- Guo, W., et al., Non-mass Breast Lesions: Could Multimodal Ultrasound Imaging Be Helpful for Their Diagnosis? Diagnostics (Basel), 2022. 12(12). [Google Scholar]
- Shahzad, R., et al., Diagnostic value of strain elastography and shear wave elastography in differentiating benign and malignant breast lesions. Ann Saudi Med, 2022. 42(5): p. 319–326. [Google Scholar]
- Boca Bene, I., S.M. Dudea, and A.I. Ciurea, Contrast-Enhanced Ultrasonography in the Diagnosis and Treatment Modulation of Breast Cancer. J Pers Med, 2021. 11(2). [Google Scholar]
- Dan, Q., et al., Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review. NPJ Precis Oncol, 2024. 8(1): p. 21. [Google Scholar]
- Jabeen, K., et al., Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion. Sensors (Basel), 2022. 22(3). [Google Scholar]
- Tang, Y., et al., Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study. Quant Imaging Med Surg, 2022. 12(2): p. 1223–1234. [Google Scholar]
- Li, S.Y., et al., Determining whether the diagnostic value of B-ultrasound combined with contrast- enhanced ultrasound and shear wave elastography in breast mass-like and non-mass-like lesions differs: a diagnostic test. Gland Surg, 2023. 12(2): p. 282–296. [Google Scholar]
- Qian, X., et al., Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat Biomed Eng, 2021. 5(6): p. 522–532. [Google Scholar]
- Huang, R., et al., AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound. Med Image Anal, 2021. 72: p. 102137. [Google Scholar]
- Liu, Y., et al., FAMF-Net: Feature Alignment Mutual Attention Fusion With Region Awareness for Breast Cancer Diagnosis via Imbalanced Data. IEEE Trans Med Imaging, 2025. 44(3): p. 1153–1167. [Google Scholar]
- Lin, X., et al. Beyond adapting SAM: Towards end- to-end ultrasound image segmentation via auto prompting. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2024. Springer. [Google Scholar]
- Ruan, J., et al. EGE-UNet: An Efficient Group Enhanced UNet for Skin Lesion Segmentation. 2023. Cham: Springer Nature Switzerland. [Google Scholar]
- Shan, J., et al., Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. Ultrasound Med Biol, 2016. 42(4): p. 980–8. [Google Scholar]
- Yang, K., et al., Development and validation of a nomogram for discriminating between benign and malignant breast masses by conventional ultrasound and dual-mode elastography: a multicenter study. Quant Imaging Med Surg, 2023. 13(2): p. 865–877. [Google Scholar]
- Moustafa, A.F., et al., Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer. Diagnostics (Basel), 2020. 10(9). [Google Scholar]
- Yuan, S., et al., Rmau-net: Breast tumor segmentation network based on residual depthwise separable convolution and multiscale channel attention gates. Applied Sciences, 2023. 13(20): p. 11362. [Google Scholar]
- Pramanik, P., et al., DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images. PLoS One, 2024. 19(5): p. e0303670. [Google Scholar]
- Latha, M., et al., Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI. BMC Med Imaging, 2024. 24(1): p. 230. [Google Scholar]
- Kirillov, A., et al. Segment anything. in Proceedings of the IEEE/CVF international conference on computer vision. 2023. [Google Scholar]
- Tu, Z., et al., Ultrasound sam adapter: Adapting sam for breast lesion segmentation in ultrasound images. arXiv preprint arXiv:2404.14837, 2024. [Google Scholar]
- Ma, J., et al., Segment anything in medical images. Nat Commun, 2024. 15(1): p. 654. [Google Scholar]
- Misra, S., et al., Ensemble transfer learning of elastography and B-mode breast ultrasound images. arXiv preprint arXiv:2102.08567, 2021. [Google Scholar]
- Wang, D., M. Xue, and H. Wang, TMAN: A Triple Morphological Feature Attention Network for Fine- Grained Classification of Breast Ultrasound Images. Journal of Imaging Informatics in Medicine, 2025. [Google Scholar]
- Long, B., Y. Guan, and M. Holden. A Two-Stage Neural Network Model for Breast Ultrasound Image Classification. in 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE). 2023. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

