Semi-Supervised Learning For Cardiac Left Ventricle Segmentation Using Conditional Deep Generative Models as Prior
Bibliographic record
Abstract
Accurate segmentation of left ventricle (LV) in apical four chamber echocardiography cine is a key step in cardiac functionality assessment. Cardiologists roughly annotate two frames in the cardiac cycle, namely, the end-diastolic and end-systolic frames, as part of their clinical workflow, limiting the annotated data to less than 5% of the frames in the cardiac cycle. In this paper, we propose a semi-supervised learning algorithm to leverage the unlabeled data to improve the performance of LV segmentation algorithms. This approach is based on a generative model which learns an inverse mapping from segmentation masks to their corresponding echo frames. This generator is then used as a critic to assess and improve the LV segmentation mask generated by a given segmentation algorithm such as U-Net. This semi-supervised approach enforces a prior on the segmentation model based on the perceptual similarity of the generated frame with the original frame. This approach promotes utilization of the unlabeled samples, which, in turn, improves the segmentation accuracy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".