Redundant co-training: Semi-supervised segmentation of medical images using informative redundancy
Bibliographic record
Abstract
Pseudo-labeling, consistency regularization, and co-training are common paradigms for semi-supervised learning. In this paper, we propose a novel method based on co-training and pseudo-labeling for the semi-supervised segmentation of the left ventricle. Our co-training strategy is novel and unlike most previous works does not rely on using multiple-view datasets, performing weak/strong augmentations on the input images or perturbations on the networks. We proposed creating redundant labels by utilizing the provided ground-truths and training networks segmenting different overlapping regions corresponding to the created labels. Although the new labels seem to be redundant, we demonstrated that they provide valuable information to the networks. The predictions of the redundant networks (which are trained on the redundant labels) can be used in the pixels where the primary network’s predictions are not reliable. This enables extracting a secondary source of information without requiring any additional ground-truths. The common practice in pseudo-labeling is using the reliable predictions of the unlabeled data and discarding the unreliable ones. However, we proposed utilizing predictions from the redundant networks to generate pseudo-labels for the unreliable pixels in the primary network’s predictions, rather than simply discarding them. We validated our method on two left ventricle segmentation datasets, and it surpassed the state-of-the-art semi-supervised learning approaches. Furthermore, we conducted extensive studies to analyze the proposed method from different aspects. Implementation of our work is available at https://github.com/behnam-rahmati/redundant-cotraining .
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".