<title>Unsupervised partial volume estimation using 3D and statistical priors</title>
Bibliographic record
Abstract
Our main objective is to compute the volume of interest of images from magnetic resonance imaging (MRI). We suggest a method based on maximum a posteriori. Using texture models, we propose a new partial volume determination. We model tissues using generalized gaussian distributions fitted from a mixture of their gray levels and texture information. Texture information relies on estimation errors from multiresolution and multispectral autoregressive models. A uniform distribution solves large estimation errors, when dealing with unknown tissues. An initial segmentation, needed by the multiresolution segmentation deterministic relaxation algorithm, is found using an anatomical atlas. To model the a priori information, we use a full 3-D extension of Markov random fields. Our 3-D extension is straightforward, easily implemented, and includes single label probability. Using initial segmentation map and initial tissues models, iterative updates are made on the segmentation map and tissue models. Updating tissue models remove field inhomogeneities. Partial volumes are computed from final segmentation map and tissue models. Preliminary results are encouraging.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.027 |
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".