{
  "id": 14047,
  "url": "https://arxiv.org/abs/2607.24453v1",
  "title": "ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image",
  "summary": "Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly. We study retinal vessel segmentation in an extreme semi-supervised setting with one annotated image and a pool of unlabeled images. We propose ESRVS, which selects a representative reference image for manual annotation and transfers vessel cues using target-domain-adapted DINOv3 features. ESRVS constructs a multi granular vessel prototype, combines prototype-simil",
  "authors": "Mingzhi Xu, Yizhe Zhang",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T13:57:07.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14047",
  "original_url": "https://arxiv.org/abs/2607.24453v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}