Eigenrank ranks scans by the largest eigenvalue of a Dice-similarity matrix between committee models, and this ranking improves training-data selection and predicts segmentation failure on spine MRI.
Sensitive quantitative predictions of peptide-mhc binding by a ‘query by com- mittee’artificial neural network approach,
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EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation
Eigenrank ranks scans by the largest eigenvalue of a Dice-similarity matrix between committee models, and this ranking improves training-data selection and predicts segmentation failure on spine MRI.