Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol
Bibliographic record
Abstract
Computer-aided diagnosis (CAD) tools using MR images have been largely developed for disease burden quantification, patient diagnosis and follow-up. Newer CAD tools, based on machine learning techniques, often require large and heterogeneous data-sets to provide accurate and generalizable results. Commonly multi-center MR imaging data-sets are used. Typically, collection of these data-sets require adherence to an appropriate experimental protocol in order to assure that findings are due to a pathology and not due to variability in image quality or acquisition parameters across scanners and/or imaging centers. We compared different experimental training protocols used with a representative CAD tool (in this work, designed to identify Alzheimer’s disease (AD) patients from normal control (NC) subjects) using public multi-center data-sets. We examined: 1) subsets of the data-set that were acquired on the same scanner (simulating a single site homogeneous data-set), 2) a traditional cross validation framework (i.e., randomly splitting the data-set into training and testing sets irrespective of centre), and 3) a site-wise cross validation framework, in which training and testing data were differentiated by center using a leave one center out per iteration method. Results achieved with the homogeneous data-set, traditional cross-validation and site-wise cross validation differed (p = 0.0005): 100.0% (i.e., no misclassifications), 99.6% and 97.3% accuracy rates, respectively, even when the same image data-set, features and classifier were used. The lowest accuracy was observed with site-wise cross validation, the only protocol with no site-wise contamination between training and testing samples.
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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 | 0.076 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".