Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information
Bibliographic record
Abstract
A fully automatic Bayesian framework for multiple sclerosis (MS) lesion classification is presented, using posterior probability distributions and entropy values to classify normal and lesion tissue. Spatial variability in intensities of multimodal MR images over the brain is explicitly modeled by building region-specific multivariate likelihood distributions. Local smoothness is ensured by incorporating neighboring voxel tissue information using Markov Random fields. A probabilistic measure of confidence for the classification is then presented, which can also be used to assess disease burden. The method was tested on 10 patients with MS by comparing automatically classified lesions, with and without regional information, to manual classifications by five expert raters using volume count and overlap. Results improve with the incorporation of spatial information, and are comparable to manual classifications. This method also enables a more accurate classification in the posterior fossa, where no other method reports success.
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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".