OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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UNVERDICTED 2representative citing papers
Evo-PU enhances protein function prediction by modeling evolutionary mutation processes within a positive-unlabeled framework to correct for survivorship bias in single-organism sequence data.
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OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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Better Protein Function Prediction by Modeling Survivorship Bias
Evo-PU enhances protein function prediction by modeling evolutionary mutation processes within a positive-unlabeled framework to correct for survivorship bias in single-organism sequence data.