Annotation-efficient deep learning detection and measurement of mediastinal lymph nodes in CT
Our semi-supervised method requires one-fourth to one-eighth less annotations to achieve a performance to supervised models trained on the same dataset for the automatic measurement of mediastinal LNs in chest ceCT. Using pseudolabels with anatomical filtering may be effective to overcome the challenges of the development of AI-based solutions in radiology.
Authors
- Leo Joskowicz (ORCID: https://orcid.org/0000-0002-3010-4770)
- Jacob Sosna (ORCID: https://orcid.org/0000-0002-2400-1863)
- Yusef Azraq (ORCID: https://orcid.org/0000-0002-2239-8203)
- Richard J. Lederman
- A. A. OLESINSKI
Institutions
- Hebrew University of Jerusalem (IL)
- Hadassah Medical Center (IL)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-09-13
- DOI
- https://doi.org/10.1007/s11548-025-03513-y
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hebrew University of Jerusalem