arXiv (All)
open access
UniReg: Conditional Unified Model for Medical Image Registration
Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However, existing approaches suffer from poor generalization across diverse clinical scenarios, requiring the laborious development of multiple isolated networ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan#973245
- MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting#974953
- A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy#1051129
- A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy#1028755
- The Devil is in the Details: Automated Dense Landmark Correspondence and an Interpretable Similarity Score for Verifying Thoracic CT Deformable Image Registration#1038690
Record · ID 1013405
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive