Spatial codification of label predictions in multi-scale stacked sequential learning: A case study on multi-class medical volume segmentation

Frederic Sampedro, Sergio Escalera

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    © The Institution of Engineering and Technology 2015. In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
    Original languageEnglish
    Pages (from-to)439-446
    JournalIET Computer Vision
    Volume9
    Issue number3
    DOIs
    Publication statusPublished - 1 Jan 2015

    Fingerprint Dive into the research topics of 'Spatial codification of label predictions in multi-scale stacked sequential learning: A case study on multi-class medical volume segmentation'. Together they form a unique fingerprint.

  • Cite this