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
MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.
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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Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.