{
  "id": 7278,
  "url": "https://arxiv.org/abs/2603.12988v1",
  "title": "Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning",
  "summary": "We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge at the PHAROS-AIF-MIH Workshop (CVPR 2026). The challenge requires classifying CT scans into four categories -- Healthy, COVID-19, Adenocarcinoma, and Squamous Cell Carcinoma -- with performance measured as the average of per-gender macro F1 scores, explicitly penalizing gender-inequitable predictions. Our approach addresses two core difficulties:",
  "authors": "Aditya Parikh, Aasa Feragen",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-13T13:42:52.000Z",
  "fetched_at": "2026-07-14T16:33:08.011Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7278",
  "original_url": "https://arxiv.org/abs/2603.12988v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}