The application of multi-instance learning based on feature reconstruction and cross-mixing in the Gleason grading of prostate cancer from whole-slide images
The FRCM-MIL model performs outstandingly in the Gleason grading task for prostate cancer WSIs, effectively distinguishing between different grades. This model has the potential to assist clinicians in formulating personalized chemotherapy and radiotherapy plans, ultimately improving treatment outcomes and demonstrating significant clinical value.
Authors
- Yu Xiao (ORCID: https://orcid.org/0000-0002-9034-3386)
- Weitian Chen (ORCID: https://orcid.org/0000-0001-7242-9285)
- Chuanbo Qin (ORCID: https://orcid.org/0000-0001-7189-5196)
- Junying Zeng
- Chaoyun Mai (ORCID: https://orcid.org/0000-0003-4353-5868)
- Weigang Yan (ORCID: https://orcid.org/0000-0002-8995-1456)
- Qianwen Wang (ORCID: https://orcid.org/0000-0003-3553-1162)
- Hao Xie (ORCID: https://orcid.org/0000-0003-3854-0849)
- Runqiang Yuan
- Hongxing Huang (ORCID: https://orcid.org/0009-0003-2461-5747)
- Zhipeng Mai
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Zhongshan People's Hospital (CN)
- Wuyi University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21037/qims-24-1985
- Citations
- 2
- Primary Topic
- Image Retrieval and Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.55