Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets
Results and benchmarks
Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets presents a generative model evaluation approach for metric (unit).
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
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No verified maintained repo yet
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- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: A rail network performance metric to capture passenger experience.
- Track assumptions and missing details in an experiment log before coding.
Time to first repro: a few days
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
5
Citations
62
References
Tasks
Metric (unit), Domain (mathematical analysis), Computer science, radiomic features, out-of-domain detection, image-to-image translation, unconditional image generation, adversarial robustness
Methods
generative model evaluation
Domains
Distribution (mathematics), Image (mathematics), Artificial intelligence, Mathematics, Computer Vision and Pattern Recognition
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