Quantifying White Matter Hyperintensities: Predicting Periventricular Fazekas Scores with Uncertainty Estimation
Bibliographic record
Abstract
White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".