arXiv (All)
open access
Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images
Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but may be missing or unreliable in imaging archives. Purpose: To develop and evaluate a fast open-source model that predicts patient and acquisition
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
- 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
- Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models#1057693
- Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos#1151310
- RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction#670577
Record · ID 974590
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