NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data
Faizan Ullah, Zaheer Abbas, Mohmmad Abrar, Farhan Amin, N. Jon Shah · 2026
Read NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data by Faizan Ullah, Zaheer Abbas, Mohmmad Abrar, Farhan Amin, N. Jon Shah (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13623914.txt.
Description
Public brain tumour scan collections feed much AI research, yet nobody checks whether their labels and metadata are tidy. Faizan Ullah and co-authors build NeuroFAIR Curator, a stewardship framework for that checking. It enriches metadata, screens image quality, and ranks which samples deserve human review. It also scores the dataset against the FAIR principles: findable, accessible, interoperable, and reusable data.
On a 2025 dataset of tumour MRI images, the pipeline processed nearly five thousand image-mask pairs. Its classifier and segmentation models scored high on accuracy, and a fused uncertainty index concentrated the most suspicious samples into a small review budget. Screening flagged about four percent of masks as statistically odd — explicitly not proof of errors. Findability and interoperability scores jumped after enrichment. The authors stress the tool judges technical tidiness, never clinical correctness.
Catalog record
| CREATOR | Faizan Ullah, Zaheer Abbas, Mohmmad Abrar, Farhan Amin, N. Jon Shah |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |