Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer-aided diagnosis (CAD) is an attractive topic in Alzheimer's disease (AD) research. Many algorithms are based on a relatively large training dataset. However, small hospitals are usually unable to collect sufficient training samples for robust classification. Although data sharing is expanding in scientific research, it is unclear whether a model based on one dataset is well suited for other data sources. Using a small dataset from a local hospital and a large shared dataset from the AD neuroimaging initiative, we conducted a heterogeneity analysis and found that different functional magnetic resonance imaging data sources show different sample distributions in feature space. In addition, we proposed an effective knowledge transfer method to diminish the disparity among different datasets and improve the classification accuracy on datasets with insufficient training samples. The accuracy increased by approximately 20% compared with that of a model based only on the original small dataset. The results demonstrated that the proposed approach is a novel and effective method for CAD in hospitals with only small training datasets. It solved the challenge of limited sample size in detection of AD, which is a common issue but lack of adequate attention. Furthermore, this paper sheds new light on effective use of multi-source data for neurological disease diagnosis.
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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 | 0.001 | 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 it