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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".