Medical Knowledge-Guided Semi-Supervised Bi-Ventricular Segmentation
Bibliographic record
Abstract
Pseudo-labeling is a well-studied semi-supervised learning approach that generates artificial labels for unlabeled data based on the predictions of an initial model trained on labeled data. Although pseudo-labeling is an effective approach for a wide range of tasks, incorporating physical knowledge concentrated on a particular organ (such as cardiac structures), can outperform the general strategy employed in pseudo-labeling. In this work, we propose to integrate different physical (medical) properties into the semi-supervised bi-ventricular segmentation task. We incorporate this knowledge as regularization terms in the loss function, uncertainty criteria for assessing the predictions, and pseudo-label modification methods. Our extracted properties are based on the physical characteristics of the ventricles and are robust to modality changes. We validated our method using the ACDC and SCD datasets. Numerical measurements confirm the success of the proposed approach. The implementation of our work is available at “https://github.com/behnamrahmati/MedicalGuided”
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".