Benchmarking Foundation Models for Renal Lesion Stratification in CT
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant but data-scarce classification tasks? Particularly in CT-based renal lesion classification, a push toward greater generalizability would be meaningful, as the field is constrained by inherently limited training data. We addressed this through a benchmark of three medical FMs on this specific task. This six-class problem spans common entities like cysts and clear cell renal cell carcinoma, alongside rare subtypes. Using a frozen feature-probing protocol, we compared FM embeddings against a handcrafted radiomics cla...
| Authors | Hartmut Häntze, Sarah de Boer, Myrthe Buser et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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