{
  "id": 17144,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1882547",
  "title": "Towards cross-center head and neck cancer detection: a multi-level domain alignment exploration",
  "summary": "IntroductionDeep learning models for head and neck cancer (HNC) detection from computed tomography (CT) hold significant promise for improving early detection—a critical priority given that 5-year survival drops from 84% for localized disease to 39% for metastatic cases. However, robust cross-center deployment remains challenging because scanner vendors, acquisition protocols, reconstruction Q15 kernels, and patient populations vary across hospitals. To address this challenge, we propose MDA-Net",
  "authors": "Jiaqi Zhao",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T00:00:00.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/17144",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1882547",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}