Bayesian generative models for knowledge transfer in MRI semantic segmentation problems

Anna Kuzina, Evgenii Egorov, Evgeny Burnaev

    Результат исследований: Вклад в журналСтатьярецензирование

    14 Цитирования (Scopus)

    Аннотация

    Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network. Our approach is compared to a pre-train approach and random initialization and obtains the best results in terms of Dice Similarity Coefficient metric for the small subsets of the Brain Tumor Segmentation 2018 database (BRATS2018).

    Язык оригиналаАнглийский
    Номер статьи844
    ЖурналFrontiers in Neuroscience
    Том13
    Номер выпускаJUL
    DOI
    СостояниеОпубликовано - 2019

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