Local discriminative characterization of MRI for Alzheimer's disease
Bibliographic record
Abstract
A novel method is proposed for characterizing Alzheimer's disease (AD) in brain MRI using local image texture features. Texture features are computed from local sub-volumes of T1-weighted MR images, and automatic classification performance is used to identify the (brain region, texture feature) combinations that are most discriminative regarding subject groups, i.e. AD vs healthy subjects. Experiments include MRI data of 124 subjects from the public OASIS database, three commonly-used texture feature types including the 3D-GLCM, 3D-DWT and LoG filters and random forest classification. The method identifies numerous (brain region, texture feature) combinations leading to high classification accuracy (> 70%), including several regions not traditionally linked to AD. These may indicate novel computational biomarkers for computer-assisted diagnosis or characterization of AD. The approach is generally applicable to other 3D data and disease contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".