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.
Kvasir-seg: A segmented polyp dataset
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2roles
background 1polarities
background 1representative citing papers
AnomalyVFM converts vision foundation models into zero-shot anomaly detectors via three-stage synthetic dataset generation plus low-rank adapters and weighted pixel loss, reaching 94.1% average image AUROC across nine datasets.
citing papers explorer
-
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.
-
AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
AnomalyVFM converts vision foundation models into zero-shot anomaly detectors via three-stage synthetic dataset generation plus low-rank adapters and weighted pixel loss, reaching 94.1% average image AUROC across nine datasets.