The framework was developed by Faizan Ullah and colleagues at Forschungszentrum Jülich, RWTH Aachen University, Arab Open University, and Yeungnam University. Its purpose is deliberately different from the usual goals of medical imaging AI. Rather than deciding whether a tumor is present or delineating its boundaries for clinical use, NeuroFAIR Curator acts as a data steward. It enriches metadata, screens technical annotation consistency, characterizes image quality, flags statistically unusual masks, prioritizes samples for human review, and scores the dataset against the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. The authors are explicit that the system does not determine the clinical correctness of tumor annotations; it surfaces computational signals that experts can then investigate.

The test case was BRISC 2025, a public dataset of 6,000 two-dimensional contrast-enhanced T1-weighted MRI images distributed as JPEG files with binary PNG segmentation masks. The images span four categories—glioma, meningioma, pituitary tumor, and no tumor—across axial, coronal, and sagittal anatomical planes. NeuroFAIR Curator generated 6,000 sample-level curation records linked with 15,586 external manifest entries, producing 21,586 unified records, and processed 4,793 image-mask pairs. The resulting manifest serves simultaneously as a dataset description and as a curation log, carrying inferred sequence descriptors, anatomical planes, image statistics, mask statistics, consistency checks, quality flags, and model-derived signals for every sample.

Two neural networks form the computational core of the label and mask screening. A ResCNNClassifier, trained for 120 epochs with class weighting, label smoothing, MixUp augmentation, and an exponential moving average of weights, achieved 98.30 percent accuracy, 98.53 percent macro F1, and 99.79 percent macro AUC on the held-out test set. Per-class F1 ranged from 0.9736 for meningioma to a perfect 1.0000 for the no-tumor class. The classifier’s confidence and uncertainty signals, together with any disagreement between the recorded label and the model prediction, are stored as curation signals rather than diagnostic outputs. A UNet segmenter, trained for 90 epochs with a combined binary cross-entropy, Dice, and focal loss, achieved a Dice score of 82.30 percent, an IoU of 72.69 percent, precision of 83.64 percent, recall of 84.61 percent, a Hausdorff distance of 7.84 pixels, and an HD95 of 4.11 pixels at a validation-selected threshold of 0.95. Boundary quality reached 0.9964.

Explainability was built into the workflow. Grad-CAM saliency maps were generated from the final convolutional layer for the 20 most uncertain test images, and their fidelity was quantified using deletion area under the curve, or DAUC, which measures how much class confidence drops when the most salient pixels are progressively deleted. The mean DAUC was 0.2996, with per-class means of 0.3145 for glioma and 0.2933 for meningioma. Lower values indicate that the importance ordering is faithful to what the network actually relies on. The authors stress that these maps quantify faithfulness of the explanations but do not constitute clinical localization validation.

One of the most intriguing findings concerns mask morphology. The framework stratified supplied mask-area ratios by tumor class and anatomical plane and applied class- and plane-specific 1.5 times interquartile-range limits. This exploratory screening flagged 181 of 4,793 masks, or 3.78 percent, as statistically atypical. The authors are careful to note that these flags are not confirmed annotation errors; they may reflect tumor size, slice position, morphology, annotation convention, or genuine annotation problems, and without clinical-reader assessment these explanations cannot be distinguished. The flags are retained as review candidates rather than grounds for automatic exclusion, a stance consistent with prior research showing that inter-observer variability in tumor delineation can be substantial.

Technical integrity checks told a reassuring story about the dataset’s file-level organization. No missing tumor masks, empty tumor masks, dimension mismatches, filename inconsistencies, unknown planes, or corrupted samples were detected. Image-quality screening, by contrast, revealed a small but real tail of problematic images: 51 low-contrast cases, 60 blur outliers, 208 intensity outliers, and 180 anomalies detected by an Isolation Forest. Across the corpus, 456 samples carried at least one flag and 184 carried multiple flags simultaneously, while 5,544 samples had zero flags.

The framework’s most distinctive contribution is the Curation Uncertainty Index, or CUI, a transparent seven-component score that fuses classification uncertainty, segmentation uncertainty, mask abnormality, metadata incompleteness, image-quality anomaly, image-mask mismatch, and metadata-validation error. The baseline weights were prespecified as a hierarchical prior rather than fitted to test data, with model uncertainty receiving 0.36, statistical and structural mask evidence 0.16, reuse-quality evidence 0.28, and sparse technical validation signals 0.20. A one-at-a-time sensitivity analysis perturbed each weight by up to 50 percent and showed that the ranking remained stable under most perturbations, with Spearman correlations above 0.99. The largest ranking change occurred when segmentation uncertainty was reduced by half, highlighting which signals drive the prioritization.

Active curation prioritization demonstrated the practical value of the CUI. At a review budget of just 1 percent of the dataset, the CUI ranking captured 49 of the 456 uniquely flagged samples, a 10.75 percent capture rate that represents a 99 percent reduction in review workload compared with random inspection. At budgets of 5 and 10 percent, capture rose to 15.13 and 17.11 percent respectively. The CUI assigned 5,001 samples, or 83.35 percent, to low curation priority and 999 samples, or 16.65 percent, to moderate priority, giving curators a concrete, defensible ordering for their limited expert time.

The FAIR readiness assessment, scored on an 18-sub-criterion rubric, showed the largest gains where the dataset was weakest. Findability improved from 87.50 to 100.00 and Interoperability from 36.00 to 91.98, reflecting the addition of structured descriptors, harmonized metadata, quality indicators, and provenance fields. Accessibility and Reusability remained unchanged at 95.00 and 95.98 under the declared rubric, a pattern the authors attribute to the specific instrument and dataset rather than a universal result. Taken together, NeuroFAIR Curator converts a widely reused public brain tumor MRI corpus into a metadata-enriched, technically checked, quality-screened, review-prioritized, and FAIR-aligned neuroinformatics resource. The authors note that future work should evaluate the framework with blinded expert review, multicenter DICOM or NIfTI datasets, three-dimensional annotations, and external calibration across different scanners and acquisition protocols, since the operating threshold and CUI weights are specific to the BRISC 2025 validation distribution rather than universal parameters.

Subject of Research: AI-assisted metadata enrichment, quality screening, curation prioritization, and FAIR readiness assessment of public brain tumor MRI data

Article Title: NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data

Article References: Ullah, F., Abbas, Z., Abrar, M., Amin, F., & Shah, N. J. (2026). NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data. Frontiers in Neuroinformatics, 20, Article 1907796. https://doi.org/10.3389/fninf.2026.1907796

Image Credits: AI Generated

DOI: 10.3389/fninf.2026.1907796

Keywords: brain tumor MRI, FAIR data, metadata enrichment, data curation, neuroinformatics, annotation integrity, uncertainty quantification, active learning, image quality control, BRISC 2025, Curation Uncertainty Index, deep learning