Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 6 | |
| Section | Intelligent Computing in Healthcare and Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20268401014 | |
| Published online | 06 April 2026 | |
- World Health Organization, Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer [Google Scholar]
- X. Xiong, L.W. Zheng, Y. Ding, et al., Breast cancer: Pathogenesis and treatments. Sig. Transduct. Target Ther. 10, 49 (2025) [Google Scholar]
- K.M., T.K., S.K., Novel deep CNN model based breast cancer classification. in Proceedings of the Int. Conf. on Computing Methodologies and Communication (ICCMC), 524–529 (Year not provided) [Google Scholar]
- J. Donnelly, L. Moffett, A.J. Barnett, H. Trivedi, F. Schwartz, J. Lo, C. Rudin, AsymMirai: Interpretable mammography-based deep learning model for 1–5-year breast cancer risk prediction. Radiology 310(3), e232780 (2024) [Google Scholar]
- F. Galati, V. Rizzo, R.M. Trimboli, E. Kripa, R. Maroncelli, F. Pediconi, MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 4(1), 20220002 (2022) [Google Scholar]
- A. Akbar, S. Han, N. Urr Rehman, et al., Explainable breast cancer prediction from 3- dimensional dynamic contrast-enhanced magnetic resonance imaging. Appl. Intell. 55, 901 (2025) [Google Scholar]
- R. Lo Gullo, R.E. Ochoa-Albiztegui, J. Chakraborty, S.B. Thakur, M. Robson, M.S. Jochelson, ... K. Pinker, Development of an MRI radiomic machine-learning model to predict triple-negative breast cancer based on fibroglandular tissue of the contralateral unaffected breast in breast cancer patients. Cancers 16(20), 3480 (2024) [Google Scholar]
- A. Bhushan, A. Gonsalves, J.U. Menon, Current state of breast cancer diagnosis, treatment, and theranostics. Pharmaceutics 13(5), 723 (2021) [Google Scholar]
- A. Ashraf, A.E. Nagib, H. Mohamed, Enhancing breast cancer diagnosis with Vision Transformer-based ultrasound image classification. in Proceedings of the Novel Intelligent and Leading Emerging Sciences Conference (NILES), 161–165 (Year not provided) [Google Scholar]
- B. Shareef, M. Xian, A. Vakanski, H. Wang, Breast ultrasound tumor classification using a hybrid multitask CNN-Transformer network. in Proceedings of the Med. Image Comput. Comput. Assist. Interv. (MICCAI), 344–353 (Year not provided) [Google Scholar]
- E. Dhamija, M. Gulati, S.V.S. Deo, A. Gogia, S. Hari, Digital breast tomosynthesis: An overview. Indian J. Surg. Oncol. 12(2), 315–329 (2021) [Google Scholar]
- Y. Alashban, Breast cancer detection and classification with digital breast tomosynthesis: A two-stage deep learning approach. Diagn. Interv. Radiol. 31(3), 206–214 (2025) [Google Scholar]
- I. Kassis, D. Lederman, G. Ben-Arie, et al., Detection of breast cancer in digital breast tomosynthesis with vision transformers. Sci. Rep. 14, 22149 (2024) [Google Scholar]
- J. Chen, T. Pan, Z. Zhu, et al., A deep learning-based multimodal medical imaging model for breast cancer screening. Sci. Rep. 15, 14696 (2025) [Google Scholar]
- L. Wang, Mammography with deep learning for breast cancer detection. Front. Oncol. 14, 1281922 (2024) [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.

