A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI
We successfully developed and externally validated the transformer-based PBTC-Transnet fusion model for the effective classification of primary bone tumors. This model, rooted in incomplete multimodal images and clinical characteristics, effectively mirrors real-life clinical scenarios, thus enhancing its strong clinical practicability.
Authors
- Shisi Li (ORCID: https://orcid.org/0009-0009-7651-8336)
- Menghong Wang (ORCID: https://orcid.org/0000-0001-7349-699X)
- Chuanpu Li
- Qinghai Zeng (ORCID: https://orcid.org/0000-0002-6747-1486)
- Rui Zhang (ORCID: https://orcid.org/0000-0002-5399-7057)
- Qiang Ye
- Wei Yang (ORCID: https://orcid.org/0000-0003-0178-7135)
- Yinghua Zhao (ORCID: https://orcid.org/0000-0001-9969-1670)
- Liwen Song (ORCID: https://orcid.org/0000-0002-1291-266X)
- Tan Lilian
- Zhuoyao Xie
- Xiaqing Chen
Institutions
- Third Affiliated Hospital of Southern Medical University (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1186/s40644-024-00784-7
- Citations
- 25
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.11
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province