CERS integrates LLM-generated CoT reasoning, a knowledge pool, semantic reference selection, and a multi-scale attention module to improve semi-supervised medical image segmentation beyond visual pattern matching.
Co-training with high- confidence pseudo labels for semi-supervised medical image segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
UniSemAlign aligns text and prototype representations with visual features to generate better supervision signals for semi-supervised segmentation, reporting Dice gains of up to 8.6% on CRAG with 10% labels.
citing papers explorer
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Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
CERS integrates LLM-generated CoT reasoning, a knowledge pool, semantic reference selection, and a multi-scale attention module to improve semi-supervised medical image segmentation beyond visual pattern matching.
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UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation
UniSemAlign aligns text and prototype representations with visual features to generate better supervision signals for semi-supervised segmentation, reporting Dice gains of up to 8.6% on CRAG with 10% labels.