{
  "id": 6859,
  "url": "https://arxiv.org/abs/2603.21760v1",
  "title": "Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration",
  "summary": "Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing long-range anatomical correspondence and maintaining deformation consistency. In this work, we present a cycle inverse-consistent transformer-based framework for deformable brain MRI registration. Th",
  "authors": "Jiaqi Shang, Haojin Wu, Yinyi Lai, Zongyu Li, Chenghao Zhang, Jia Guo",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-23T09:53:06.000Z",
  "fetched_at": "2026-07-14T16:32:45.897Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6859",
  "original_url": "https://arxiv.org/abs/2603.21760v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}