AUCp selects inference models for unsupervised abnormality detection by computing AUC after labeling all test samples as positive, shown to outperform conventional metrics when normal training data is representative.
Brainomaly: Unsuper- vised neurologic disease detection utilizing unannotated t1-weighted brain mr images
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection
AUCp selects inference models for unsupervised abnormality detection by computing AUC after labeling all test samples as positive, shown to outperform conventional metrics when normal training data is representative.