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.
Qubiq: Uncertainty quantification for biomedical image segmentation challenge
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Introduces an attention-based prototype calibration framework for few-shot multi-rater medical image segmentation to model rater variability.
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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.
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Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
Introduces an attention-based prototype calibration framework for few-shot multi-rater medical image segmentation to model rater variability.