HAL (France)
open access
NeuroHarm‑Kit: An open‑source toolbox for benchmarking deep‑learning harmonization of multi‑site T1‑weighted MRI
Multi‑site magnetic resonance imaging (MRI) studies enable studying brain structure across diverse populations, but scanner‑related variability remains a critical barrier to pooled analyses. Here, we introduce NeuroHarm‑Kit, the first open‑source, end‑to‑end toolbox that unifies state‑of‑the‑art dee
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
- Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation#1105267
- An Explainable Deep Learning Framework for Imaging Genetics: Deriving Brain-Genotype Scores From MRI to Link Genetic Variation, Brain Structure, and Cognition#264604
- Conditional Flow Matching for Cross-Field MRI Harmonisation#820954
- A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.#150302
- Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI#793026
Record · ID 1039080
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