Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

• URL: http://arxiv.org/abs/2005.12639v2
• Date: Wed, 27 May 2020 19:54:19 GMT
• Title: Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
• Authors: Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
• Abstract summary: 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- 2018 database.
• Score: 15.24006130659201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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