Superpixel-Guided Graph-Attention Boundary GAN for Adaptive Feature Refinement in Scribble-Supervised Medical Image Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Fully supervised medical image segmentation still relies on labor-intensive, pixel-level annotations, which limits scale across cohorts and imaging settings. Scribble supervision reduces this burden, yet many CNN-based methods struggle under sparse labels due to weak global context and poor boundary handling. We address these issues with SGGAB-GAN, a scribble-supervised framework that uses adversarial learning, residual attention, and an enhanced feature pipeline built upon two modules: the Superpixel-Guided Graph-Attention Boundary (SGGAB) block and the Adaptive Feature Refinement Block (AFRB). First, the SGGAB block propagates limited scribble cues over a superpixel graph and reinjects boundary information, yielding crisp edges even with few annotations. Second, the AFRB fuses global context with local detail and works with residual attention gates to focus on anatomically relevant regions. On ACDC and MSCMRseg, SGGAB-GAN attains average Dice scores of 0.902 and 0.871, respectively, outperforming scribble-based methods such as ScribFormer and CycleMix while narrowing the gap to full supervision to under 2%. These results indicate that SGGAB-GAN provides high-quality segmentation at a fraction of the labeling cost, making it a scalable choice.
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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 | 0.001 |
| Open science | 0.001 | 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 it