Additional file 1 of Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation
Bibliographic record
Abstract
Additional file 1: Table S1. Table of comparison between all methods in Dataset 1 (MMWHS). The percentage of supervision is specified in brackets. Each entry represents Dice (odd rows) and ASD (even rows) average results across entire dataset. Best results are highlighted in red (Dice), and green (ASD). Table S2. Table of comparison between all methods in Dataset 2 (HRMRA). The percentage of supervision is specified in brackets. Each entry represents Dice (odd rows) and ASD (even rows) average results across entire dataset. Best results are highlighted in red (Dice), and green (ASD). Table S3. Table containing metrics for volume measurements (mL) obtained from label maps. The results are reported using avg ± std signed differences (RMSE) between ground truth volumes (top row) and predicted volumes. The second column refers to the supervision level adopted in the experiment. The best result per each method is highlighted in bold, and the best result overall is color-coded. MMWHS Dataset. Figure S1. Results grouped by label. In each boxplot, statistical analysis is conducted between experiments obtained by different methods, as per legend on the top left corner. Dashed brackets for p <= 5.00e−02, square brackets for p <= 1.00e−03. HRMRA Dataset. Figure S2. Results grouped by label. In each boxplot, statistical analysis is conducted between experiments obtained by different methods, as per legend on the top left corner. Dashed brackets for p <= 5.00e-02, square brackets for p <= 1.00e-03. MMWHS Dataset.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.529 | 0.161 |
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".