A dual-branch deep learning network for circulating tumor cells classification
The proposed framework enhances CTC identification accuracy and efficiency, demonstrating strong clinical applicability. Its output results can be directly utilized for prognosis without manual intervention, offering significant potential for personalized therapy.
Authors
- Yanfang Liang (ORCID: https://orcid.org/0009-0006-6521-452X)
- Jincheng Zeng (ORCID: https://orcid.org/0000-0001-6196-3917)
- Danni Wang (ORCID: https://orcid.org/0009-0008-7501-6601)
- Ruoxi Hong (ORCID: https://orcid.org/0000-0002-2919-1467)
- Jiaquan Lin
- Cong Li (ORCID: https://orcid.org/0000-0001-7760-3254)
- Chao Han (ORCID: https://orcid.org/0000-0001-7885-1903)
- G. M. Huang
Institutions
- Sun Yat-sen University (CN)
- Guangdong Medical College (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Key Laboratory of Guangdong Province (CN)
- Songshan Lake Materials Laboratory (CN)
- Dongguan People’s Hospital (CN)
- Beike Biotechnology (China) (CN)
- Sun Yat-sen University Cancer Center (CN)
- Kai Biotech (South Korea) (KR)
- Tang Hospital (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1186/s12967-025-07057-2
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province