A hybrid approach for enhancing pseudo-labeling in medical images through pseudo-label refinement
Bibliographic record
Abstract
Segmentation of medical images is critical for the evaluation, diagnosis, and treatment of various medical conditions. While deep learning-based approaches are the dominant methodology, they rely heavily on abundant labeled data and face significant challenges when data is limited. Semi-supervised learning methods mitigate this issue but there are still some challenges associated with them. Additionally, these approaches can be improved specifically for medical images considering their unique properties (e.g., smooth boundaries). In this work, we adapt and enhance the well-established pseudo-labeling approach specifically for medical image segmentation. Our exploration consists of modifying the network's loss function, pruning the pseudo-labels, and refining pseudo-labels by integrating traditional image processing methods with semi-supervised learning. This integration enables traditional segmentation techniques to complement deep semi-supervised methods, particularly in capturing fine edges where deep models often struggle. It also incorporates the smoothness of the edges in the segmentation and achieves a balance between deep learning and traditional methods through tunable parameters. Moreover, to address the problem of noisy or unreliable pseudo-labels, we utilize uncertainty-based pixel-level and image-level pruning of the pseudo-labels using a specific loss function, thereby improving the accuracy and robustness of the segmentation. We evaluated our approach on three different datasets from two imaging modalities (CT and MRI) and demonstrated its superior performance, highlighting its accuracy and robustness in the presence of limited labeled data. With only 15% of the labeled data, on the Sunnybrook Cardiac dataset, our approaches increased endocardium segmentation accuracy from 82.1% to 87.5%, and epicardium segmentation from 82.5% to 86.7%. On the COVID-19 CT lung and infection segmentation dataset, our approach improved left lung segmentation accuracy from 72.5% to 79.3%, and right lung segmentation from 75.8% to 81.6% when using only 15% of labeled data. On the Automated Cardiac Diagnostic Challenge dataset, with just 10% of labeled data, our approach increased endocardium segmentation from 91% to 93.7%, myocardium from 69.8% to 74.5%, and right ventricle from 76.7% to 82.1%. Our codes will be published in https://github.com/behnam-rahmati .
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".