Detecting Alzheimer’s disease by morphological MRI using hippocampal grading and cortical thickness
Bibliographic record
Abstract
Structural MRI is an important imaging biomarker in Alzheimer’s disease as the cerebral atrophy has been shown to closely correlate with cogni-tive symptoms. Recognizing this, numerous methods have been developed for quantifying the disease related atrophy from MRI over the past decades. Special effort has been dedicated to separate AD related modifications from normal ag-ing for the purpose of early detection and prediction. Several groups have re-ported promising results using automatic methods; however, it is very difficult to compare these methods due to varying cohorts and different validation frameworks. To address this issue, the public challenge on Computer-Aided Di-agnosis of Dementia based on structural MRI data (CADDementia) was pro-posed. The challenge calls for accurate classification of 354 MRI scans collect-ed among AD patients, subjects with mild cognitive impairment and cognitively normal control. The true diagnosis is hidden from the participating groups, thus making the validation truly objective. This paper describes our proposed meth-od to automatically classify the challenge data along with a validation on 30 scans with known diagnosis also provided for the challenge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".