{
  "id": 5595,
  "url": "https://arxiv.org/abs/2604.18444v1",
  "title": "ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification",
  "summary": "Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding label co-occurrence, long-tail class imbalance, and transfer instability under domain shift. We propose ProtoCLIP, a refinement strategy for CLIP-style VLMs that improves zero-shot discrimination through targeted data curation and distilled anchor alignment. Specifically, we construct pathology-focused training subsets with curated negative samples",
  "authors": "Florian Kittler, Sheethal Bhat, Andreas Maier",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-20T16:01:44.000Z",
  "fetched_at": "2026-07-14T16:31:53.164Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5595",
  "original_url": "https://arxiv.org/abs/2604.18444v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}