Deep cross entropy fusion for pulmonary nodule classification based on ultrasound Imagery
DCEF's multi-architecture fusion enhances diagnostic reliability for ultrasound-based nodule assessment. While promising, validation in larger multi-center cohorts is needed to address single-center data limitations. Future work will explore next-generation architectures and multi-modal integration.
Authors
- Xiang‐Jun Shen (ORCID: https://orcid.org/0000-0002-3359-8972)
- Sumet Mehta (ORCID: https://orcid.org/0000-0002-7928-8662)
- Donggang Pan
- Zhen Zhao (ORCID: https://orcid.org/0000-0002-0796-4078)
- Xian Wang (ORCID: https://orcid.org/0000-0001-6342-2700)
- Hui Zhou (ORCID: https://orcid.org/0000-0001-6702-2384)
- Jie Hou (ORCID: https://orcid.org/0000-0001-9853-1114)
- Hui Sun (ORCID: https://orcid.org/0000-0002-4896-4250)
- Wei Wang (ORCID: https://orcid.org/0009-0007-8440-3438)
Institutions
- Jiangsu University (CN)
- Affiliated Hospital of Jiangsu University (CN)
- Northern Jiangsu People's Hospital (CN)
- Yangzhou University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-04
- DOI
- https://doi.org/10.3389/fonc.2025.1514779
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Jiangsu University