MRI brain analysis testbed (BAT) : methodology and automatic validation pipeline
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is extensively used in brain imaging research and clinical diagnostics. Increasingly, automated image processing algorithms are used for identification of tissue types within the image, such as gray matter, white matter and cerebro-spinal fluid. There is a wide range of algorithms, which vary in speed and accuracy, and it is often difficult to compare their performance in any objective and controlled fashion. The goal of this research was to design an automatic, generic, standard, extensible pipeline for objective and quantitative validation of MRI tissue classification algorithms and their processing pipelines. The main issues and requirements, for objective validation of different algorithms, are the use of common terminology, methodology, standard validation data sets, corresponding ground truth, validation metrics and statistical foundation. Based on those requirements, an automatic Brain Analysis Testbed (BAT) was developed to determine an objective evaluation score for MRI processing method. BAT supports Montreal Neurological Institute on-site or off-site processing of MRI data, accessible by a web interface (http://www.bic.mni.mcgill.ca/validation/). Validation results are stored in the BAT database permanently, allowing the comparison of newly developed processing methods with existing ones. Furthermore, BAT can be used to determine the optimal classification parameters, or the best classifier algorithm for a specific MRI classification purpose, simply by searching the BAT database. The main purposes and principles of BAT are demonstrated with some practical MRI processing examples.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".