{
  "id": 6953,
  "url": "https://arxiv.org/abs/2603.19957v2",
  "title": "HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction",
  "summary": "Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters add",
  "authors": "Ruicheng Yuan, Zhenxuan Zhang, Anbang Wang, Liwei Hu, Xiangqian Hua, Yaya Peng et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-20T13:58:02.000Z",
  "fetched_at": "2026-07-14T16:32:50.148Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6953",
  "original_url": "https://arxiv.org/abs/2603.19957v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}