Iterative multi-class multi-scale stacked sequential learning: Definition and application to medical volume segmentation

Frederic Sampedro, Sergio Escalera, Anna Puig

    Research output: Contribution to journalArticleResearchpeer-review

    5 Citations (Scopus)

    Abstract

    In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. © 2014 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)1-10
    JournalPattern Recognition Letters
    Volume46
    DOIs
    Publication statusPublished - 1 Sep 2014

    Keywords

    • Contextual learning
    • Machine learning
    • Medical volume segmentation
    • Multi-class problems
    • Sequential learning

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