Infrastructure logicielle visant à protéger la confidentialité du patient dans les images médicales utilisées en recherche
Bibliographic record
Abstract
Pour valider les algorithmes de traitement d’image, on doit utiliser des données venant du \nmonde réel. Ces données existent et sont accessibles électroniquement. Toutefois, cela ne doit \npas se faire au détriment du droit à la confidentialité du patient, au respect de la vie privée et \nsans obtenir le consentement du patient. Le suivi du patient peut s’échelonner sur une longue \npériode de temps. Notre objectif consiste à construire une base de données d’information nonnominative \ntout en permettant une mise à jour incrémentale de l’information qu’elle contient. \nDans ce mémoire, nous explorons différentes avenues architecturales de manière à conçevoir \nune base de données d’images qui puisse être mise à jour par incrément. Comme les images \nmédicales respectent souvent le standard DICOM, nous proposons une technique d’anonymisation \nqui suit les recommandations de DICOM.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".