Detection-guided deep learning-based model with spatial regularization for lung nodule segmentation
The proposed multitask model performs lung nodule segmentation while facilitating classification outputs with feature combination and through regularization. By utilizing TL, the model achieves strong performance even with limited training data. Its simplicity makes it adaptable and provides a foundation for further enhancements to suit other specific datasets.
Authors
- Xiaojuan Li (ORCID: https://orcid.org/0000-0003-1177-3300)
- Brian A. Xavier (ORCID: https://orcid.org/0000-0001-8147-7123)
- Michael Bolen
- Weihong Guo (ORCID: https://orcid.org/0009-0003-6098-7417)
- Mingrui Yang
- Jiasen Zhang (ORCID: https://orcid.org/0009-0007-1553-4266)
Institutions
- Cleveland Clinic (US)
- Cleveland Clinic Lerner College of Medicine (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-30
- DOI
- https://doi.org/10.21037/qims-2024-2511
- Citations
- 5
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 7.59