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Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis
[chapter]
2010
Lecture Notes in Computer Science
Supervised classifiers require manually labeled training samples to classify unlabeled objects. Active Learning (AL) can be used to selectively label only "ambiguous" samples, ensuring that each labeled sample is maximally informative. This is invaluable in applications where manual labeling is expensive, as in medical images where annotation of specific pathologies or anatomical structures is usually only possible by an expert physician. Existing AL methods use a single definition of
doi:10.1007/978-3-642-16001-1_27
fatcat:lgunzi5awnh77mgppbobu5qaxy