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.
Medical Image Analysis 70, 101979 (May 2021)
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Label dropout mitigates shortcut learning in multi-dataset partially labelled echocardiography segmentation, improving Dice scores by 62% and 25% on two cardiac structures.
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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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Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
Label dropout mitigates shortcut learning in multi-dataset partially labelled echocardiography segmentation, improving Dice scores by 62% and 25% on two cardiac structures.