VFM-SSL-BMADCC-Framework: vision foundation model and self-supervised learning based automated framework for differential cell counts on whole-slide bone marrow aspirate smears
The proposed VFM-SSL-BMADCC-Framework effectively automates differential cell counts on bone marrow aspirate smears, reducing reliance on extensive annotations and improving generalization across medical centers.
Authors
- Chengliang Wang (ORCID: https://orcid.org/0000-0003-0877-1064)
- Shirong Zhou (ORCID: https://orcid.org/0000-0002-4137-9067)
- Longrong Ran (ORCID: https://orcid.org/0009-0000-1725-7345)
- Xing Li Wu (ORCID: https://orcid.org/0000-0002-3352-6960)
- Yuanzhao Yao (ORCID: https://orcid.org/0000-0003-2504-2795)
- Yao Liu (ORCID: https://orcid.org/0000-0003-1782-7322)
- Zhongshi He (ORCID: https://orcid.org/0009-0007-3682-0464)
- Zailin Yang (ORCID: https://orcid.org/0000-0002-8587-975X)
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Southwest Hospital (CN)
- Chongqing Cancer Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/fmed.2025.1624683
- Citations
- 1
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- Centre Scientifique et Technique du Bâtiment
- Fundamental Research Funds for the Central Universities