Fully Perturbed Self-Ensemble Framework Using Cascaded Parallel CNN-Transformer for Semisupervised Medical Image Segmentation
Bibliographic record
Abstract
Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning employed in existing semi-supervised MIS (SSMIS) methods is mainly restricted to a single perturbation or a simple combination of multiple perturbations, which limits their abilities to effectively exploit the potential information from unlabeled medical images. Second, although some SSMIS methods make full use of the significant structural differences between CNN and Transformer networks for model perturbation, which causes a new conflict in that a Transformer network usually requires a large amount of labeled data for model training but only a small amount of labeled data is provided to SSMIS. In this paper, a novel fully perturbed self-ensemble framework (FPSE) using cascaded parallel CNN-Transformer is proposed to address the aforementioned problems. First, we present a fully perturbed consistency learning strategy that empowers the framework to handle complex variations through the skillful synergy of data, model, and feature perturbations, effectively exploring the potential information from unlabeled medical images. Second, we design a novel strategy of model perturbation based on the cascaded parallel CNN-Transformer structure, which maximizes the efficacy of Transformer under limited labeled data since Transformer is operated on the shallow local features extracted by CNN, thus effectively alleviating the requirement of a large number of labeled data for the proposed network architecture. Experiments demonstrate that our FPSE framework achieves remarkable results, outperforming the existing state-of-the-art (SOTA) methods by 11.5%, 0.7% and 2.4% in Dice score when labeled data accounts for only 5% (ACDC), 5% (AbdomenCT-1K) and 1% (ISIC2018), respectively. The code is available at:https://github.com/SUST-SJWen/FPSE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".