Development of a diagnostic classification model for lateral cephalograms based on multitask learning
An automatic diagnostic classification model for lateral cephalograms was established based on multitask learning to achieve simultaneous classification of eight common clinical diagnostic items. The multitask learning model achieved better classification performance and reduced the computational costs, providing a novel perspective and reference for addressing such problems.
Authors
- Beiwen Gong (ORCID: https://orcid.org/0000-0001-7096-2190)
- Yajie Wang (ORCID: https://orcid.org/0000-0003-4847-7759)
- Yuxing Bai (ORCID: https://orcid.org/0000-0002-7239-0891)
- Fan Wang (ORCID: https://orcid.org/0000-0003-3785-6024)
- Qiao Chang (ORCID: https://orcid.org/0000-0003-1116-8995)
- Feifei Zuo (ORCID: https://orcid.org/0000-0002-2016-2176)
- Shaofeng Wang (ORCID: https://orcid.org/0009-0000-1962-0048)
- Xianju Xie
Institutions
- Capital Medical University (CN)
- Beijing Zhongke Science and Technology (China) (CN)
- Beijing Obstetrics and Gynecology Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-02-15
- DOI
- https://doi.org/10.1186/s12903-025-05588-0
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Capital Medical University
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support