A harmonized probabilistic model with adaptive feature conditioning and high-frequency prompt modules disentangles acquisition artifacts from rater variability to produce personalized yet consistent multi-rater segmentations, showing SOTA results on LIDC-IDRI and NPC-170.
Inter-rater uncertainty quantification in medical image segmentation via rater-specific bayesian neural networks
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Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
A harmonized probabilistic model with adaptive feature conditioning and high-frequency prompt modules disentangles acquisition artifacts from rater variability to produce personalized yet consistent multi-rater segmentations, showing SOTA results on LIDC-IDRI and NPC-170.