{
  "id": 7486883,
  "title": "Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI",
  "url": "https://urgent.news/2026/09/14/anatomical-grounding-and-leakage-aware-multimodal-contrastive",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-09-14T17:13:12.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.15888v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irrelevant regions, and multimodal models that add clinical tables frequently rely on variables that were used to assign the diagnostic label in the first place. We study both issues with a deliberately lightweight slice-based encoder (ResNet18 with a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Scientific Reports",
        "title": "Deep learning feature fusion with transfer learning for Alzheimer’s disease classification",
        "url": "https://urgent.news/2026/09/16/deep-learning-feature-fusion-with-transfer-learning-for-alzheimers",
        "published": "2026-09-16T00:00:00.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}