S256 - Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

Anna Kuzina, Evgenii Egorov, Evgeny Burnaev

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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).
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Poster Session #3 - 9:30 - 11:00 UTC-4 (Tuesday)
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