A Novel Weakly Supervised Segmentation Approach for Rapid Left Ventricle Annotation
Bibliographic record
Abstract
In the field of medical image segmentation, convolutional neural networks stand out as a successful method. However, in order to perform well, several labeled images are required. Manual pixel-level annotation of medical images requires the presence of a well-trained expert, is time-consuming, and is expensive. Weakly supervised learning approaches aim to address these challenges.In this work, we propose a novel weakly supervised segmentation approach specifically designed for the left ventricle. By utilizing the circular shape of the left ventricle, we introduce a weak annotation framework based on concentric circles, representing pixels inside and outside the ventricle. Our weakly supervised learning approach incorporates a loss function that ignores unannotated pixels and incorporates total variation regularization for smooth predictions. Our method significantly reduces annotation time from several minutes to 5-10 seconds per scan and eliminates the need for expert presence. We validated our method on the Sunnybrook dataset and our method reached around 0.96 of the accuracy of the networks trained in fully supervised manner (with pixel-level annotations). The implementation of our work is available at "https://github.com/behnam-rahmati/LV-weaklysupervised"
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".