Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging
No single fine-tuning strategy proved universally optimal. CNNs remain dependable for low-resource scenarios, whereas FMs benefit from parameter-efficient methods when data are sufficient.
Authors
- Paolo Soda (ORCID: https://orcid.org/0000-0003-2621-072X)
- Valerio Guarrasi (ORCID: https://orcid.org/0000-0002-1860-7447)
- Filippo Ruffini (ORCID: https://orcid.org/0009-0003-9759-5492)
- Linlin Shen
- Elena Mulero Ayllón (ORCID: https://orcid.org/0009-0005-8969-7901)
Institutions
- Università Campus Bio-Medico (IT)
- Shenzhen University (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109196
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36