DVF-YOLO-Seg: A two-stage breast mass segmentation model with enhanced feature extraction and small lesion detection
DVF-YOLO-Seg significantly enhances the detection and segmentation accuracy for small breast lesions in mammography. By combining improved detection with prompt-based segmentation, this method offers a promising approach for computer-aided diagnosis in breast cancer screening.
Authors
- Yuxin Gao (ORCID: https://orcid.org/0009-0008-8604-5977)
- Mayilamu Musideke
- Abudukelimu Abulizi (ORCID: https://orcid.org/0009-0002-1407-4230)
- Mengfei Wang
- Halidanmu Abudukelimu (ORCID: https://orcid.org/0000-0002-4095-1690)
- Shuqin Wu (ORCID: https://orcid.org/0009-0003-2710-0095)
- Mayila Abudukelimu
- Mireguli Aizizi
- Gulimiremu Yehaiya
Institutions
- Xinjiang Medical University (CN)
- Yili Friendship Hospital (CN)
- Sixth Affiliated Hospital of Xinjiang Medical University (CN)
- Xinjiang University of Finance and Economics (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251374192
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Natural Science Foundation of Xinjiang Province
- National Natural Science Foundation of China