radiology-foundation-models
radiology-foundation-models is a Computer vision skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-foundation-models C (71/100) and it passed the safety scan; the rating is based on static analysis only.
Use when an imaging study must select, adapt, fine-tune, or audit a pretrained medical imaging or vision-language foundation model. Covers zero-shot evaluation, linear probing, full fine-tuning, adapters, LoRA and other parameter-efficient tuning, prompt learning, domain adaptation, 2D/3D and image-text inputs, frozen tests, strong baselines, compute reporting, calibration, uncertainty, subgroups, and external validation. Warns against training a foundation model from scratch without adequate scale.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 1,707
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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