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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

2020-05-26MIDL 2019Code Available0· sign in to hype

Anna Kuzina, Evgenii Egorov, Evgeny Burnaev

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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).

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