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Seventy Percent of the Labels Left. The Scan Model Held
FEEDS selected diverse PET/CT cases for annotation and matched fully labeled training in the authors' multi-dataset evaluation.
Summary
FEEDS selected diverse PET/CT cases for annotation and matched fully labeled training in the authors' multi-dataset evaluation.
FEEDS uses foundation-model embeddings to choose informative and diverse unlabeled whole-body scans for expert annotation in one step. Tested across AutoPET-III, DeepPSMA and an internal Dartmouth-Hitchcock dataset, the authors report performance matching training on the full labeled set while reducing annotation burden by 70 percent. The work evaluates segmentation across tracers and cancers; it does not establish clinical deployment or replace expert review.
Why it matters
FEEDS selected diverse PET/CT cases for annotation and matched fully labeled training in the authors' multi-dataset evaluation.
Limits and context
- The work evaluates segmentation across tracers and cancers; it does not establish clinical deployment or replace expert review.
Key claims
FEEDS selected diverse PET/CT cases for annotation and matched fully labeled training in the authors' multi-dataset evaluation.
Qualification: The work evaluates segmentation across tracers and cancers; it does not establish clinical deployment or replace expert review.
Evidence: source-2026-08-12-006
Sources
- arXiv preprint 2608.11076arXiv · primary research
Corrections
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