Efficient temporal feature utilization in ultrasound videos: a multi-channel deep learning framework for enhanced breast lesion differentiation
By effectively utilizing temporal information without additional computational burden, our proposed approach serves as a computationally efficient alternative to 3D models, advancing DL-based breast US analysis. It shows promising potential for delivering more accurate and accessible diagnostic solutions, making it highly applicable for clinical practice.
Authors
- Bonan Zhao (ORCID: https://orcid.org/0000-0002-5667-1722)
- Shouliang Qi (ORCID: https://orcid.org/0000-0003-0977-1939)
- Chunxiao Cui (ORCID: https://orcid.org/0000-0001-9675-9756)
- Patrice Monkam (ORCID: https://orcid.org/0000-0003-3805-9457)
- Chang Liu (ORCID: https://orcid.org/0000-0001-8033-9546)
- Tao Yu (ORCID: https://orcid.org/0000-0003-3498-248X)
- Xu Wang (ORCID: https://orcid.org/0000-0003-4678-1570)
- Dan Zhao (ORCID: https://orcid.org/0000-0002-5446-0307)
Institutions
- Qingdao University (CN)
- Affiliated Hospital of Qingdao University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1186/s12885-025-15144-2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundamental Research Funds for the Central Universities