Bayesian generative models for knowledge transfer in MRI semantic segmentation problems

Anna Kuzina, Evgenii Egorov, Evgeny Burnaev

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    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).

    Original languageEnglish
    Article number844
    JournalFrontiers in Neuroscience
    Volume13
    Issue numberJUL
    DOIs
    Publication statusPublished - 2019

    Keywords

    • 3D CNN
    • Bayesian neural networks
    • Brain lesion segmentation
    • Brain tumor segmentation
    • Transfer learning
    • Variational autoencoder

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