Deep Learning Methods for MRI Spinal Cord Gray Matter Segmentation
Bibliographic record
Abstract
disponible à https://github.com/neuropoly/spinalcordtoolbox.vii potentiel.Cependant, les mesures d'incertitude font partie d'un domaine de recherche en cours d'évolution dans le Deep Learning.En e et la plupart des méthodes fournissant une approximation médiocre ou une sous-estimation de l'incertitude épistémique présente dans ces modèles.L'imagerie médicale reste un domaine très di cile pour les modèles d'apprentissage automatique en raison des fortes hypothèses d'identité distributionnelle formulées par les algorithmes d'apprentissage statistique ainsi que de la di culté à incorporer de nouveaux biais inductifs dans ces modèles pour tirer parti de la symétrie, de l'invariance de rotation, entre autres.Néanmoins, avec la quantité croissante de données disponibles, elles o rent de grandes promesses et gagnent lentement en robustesse pour pouvoir entrer dans la pratique clinique.viii
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".