Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems.

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).

SUBMITTER: Kuzina A 

PROVIDER: S-EPMC6712162 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature