An Application for Efficient Error-Free Labeling of Medical Images

Michal Drozdzal, Santi Seguí, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, Jordi Vitrià

Research output: Contribution to journalArticleResearchpeer-review


In this chapter we describe an application for efficient error-free labeling of medical images. In this scenario, the compilation of a complete training set for building a realistic model of a given class of samples is not an easy task, making the process tedious and time consuming. For this reason, there is a need for interactive labeling applications that minimize the effort of the user while providing error-free labeling. We propose a new algorithm that is based on data similarity in feature space. This method actively explores data in order to find the best label-aligned clustering and exploits it to reduce the labeler effort, that is measured by the number of "clicks. Moreover, error-free labeling is guaranteed by the fact that all data and their labels proposals are visually revised by en expert. © Springer-Verlag Berlin Heidelberg 2013.
Original languageEnglish
Pages (from-to)1-16
JournalIntelligent Systems Reference Library
Publication statusPublished - 21 Oct 2013


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