A new quality-guided approach for semi-supervised medical image segmentation that trains a predictor on synthetic errors to enhance pseudolabel handling.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A two-stage framework adapts source models for cross-device meibomian gland segmentation using weak clinical priors and self-distillation, reaching Dice 0.716 on a 1000-to-100 image benchmark while enabling mask-free operation.
citing papers explorer
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Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
A new quality-guided approach for semi-supervised medical image segmentation that trains a predictor on synthetic errors to enhance pseudolabel handling.
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TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors
A two-stage framework adapts source models for cross-device meibomian gland segmentation using weak clinical priors and self-distillation, reaching Dice 0.716 on a 1000-to-100 image benchmark while enabling mask-free operation.