Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis

Abstract. 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 ambiguity, but there can be significant variation among individual methods. In this paper we present a consensus of ambiguity (CoA) approach to AL, where only samples which are consistently labeled as ambiguous across multiple AL schemes are selected for annotation. CoA-based AL uses fewer samples than Random Learning (RL) while exploiting the variance between individual AL schemes to efficiently label training sets for classifier training. We use a consensus ratio to determine the vari...

• Type: Journal
• Year: 2010
• Where: PRIB
• Authors: Scott Doyle, Anant Madabhushi
• Keywords: AL Methods, AL Schemes, Bioinformatics, CoA-based AL, PRIB 2010