Neighborhood attention transformer multiple instance learning for whole slide image classification
Our findings demonstrate that NATMIL significantly improves tumor classification accuracy by reducing errors associated with isolated tile analysis. The integration of contextual dependencies enhances the precision of cancer diagnosis using WSIs, highlighting NATMILs´ potential as a robust tool in pathology.
Authors
- Yong Gao (ORCID: https://orcid.org/0000-0002-7191-9351)
- Juanjuan Zhao (ORCID: https://orcid.org/0000-0001-8134-9076)
- Rukhma Aftab (ORCID: https://orcid.org/0000-0002-9557-3148)
- Qiang Yan (ORCID: https://orcid.org/0000-0001-5523-2659)
- Zia Urrehman (ORCID: https://orcid.org/0000-0002-6316-7495)
- Yue Huajie
- Faizi Mohammad Khalid
Institutions
- North University of China (CN)
- Shanxi Medical University (CN)
- China National Pharmaceutical Group Corporation (China) (CN)
- First Hospital of Shanxi Medical University (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-08-29
- DOI
- https://doi.org/10.3389/fonc.2024.1389396
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Natural Science Foundation of China
- Northwest Fisheries Science Center