Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound
Tumoral biomarkers are paramount for determining breast cancer treatment. The multi-task model can improve prediction performance, and improve interpretability in clinical practice. The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.
Authors
- Zengan Huang
- Hongping Song (ORCID: https://orcid.org/0000-0001-8764-4199)
- Ge Zhang (ORCID: https://orcid.org/0000-0003-0704-0657)
- Xin Zhang (ORCID: https://orcid.org/0000-0002-4026-4284)
- Yan Ju (ORCID: https://orcid.org/0000-0003-4588-0710)
- Yi Gao (ORCID: https://orcid.org/0000-0003-0094-8136)
- Wan-Ying Chang (ORCID: https://orcid.org/0000-0002-8357-1807)
Institutions
- Shenzhen University (CN)
- Shenzhen University Health Science Center (CN)
- Xijing Hospital (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1186/s13244-024-01810-9
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- National Natural Science Foundation of China
- Shenzhen Key Laboratory Fund